Introduction to multivariate discrimination

Introduction to multivariate discrimination
Introduction to multivariate
Balázs Kégl
LAL/LRI, CNRS & University Paris-Sud, France
March 2012
Multivariate discrimination or classification is one of the best-studied problem in machine
learning, with a plethora of well-tested and well-performing algorithms. There are also several
good general textbooks [1, 2, 3, 4, 5, 6, 7, 8, 9] on the subject written to an average engineering,
computer science, or statistics graduate student; most of them are also accessible for an average
physics student with some background on computer science and statistics. Hence, instead of
writing a generic introduction, we concentrate here on relating the subject to a practitioner experimental physicist. After a short introduction on the basic setup (Section 1) we delve into the
practical issues of complexity regularization, model selection, and hyperparameter optimization
(Section 2), since it is this step that makes high-complexity non-parametric fitting so different
from low-dimensional parametric fitting. To emphasize that this issue is not restricted to classification, we illustrate the concept on a low-dimensional but non-parametric regression example
(Section 2.1). Section 3 describes the common algorithmic-statistical formal framework that unifies the main families of multivariate classification algorithms. We explain here the large-margin
principle that partly explains why these algorithms work. Section 4 is devoted to the description
of the three main (families of) classification algorithms, neural networks, the support vector machine, and A DA B OOST. We do not go into the algorithmic details; the goal is to give an overview
on the form of the functions these methods learn and on the objective functions they optimize.
Besides their technical description, we also make an attempt to put these algorithm into a sociohistorical context. We then briefly describe some rather heterogeneous applications to illustrate
the pattern recognition pipeline and to show how widespread the use of these methods is (Section 5). We conclude the chapter with three essentially open research problems that are either
relevant to or even motivated by certain unorthodox applications of multivariate discrimination
in experimental physics.
The supervised learning setup
The goal of supervised learning is to infer a function g : X → Y from a data set
D = (x1 , y1 ), . . . , (xn , yn ) ∈ (X × Y )n
comprised of pairs of observations xi and labels yi . The components x ( j) of the d-dimensional feature
or observation vector x are either real numbers or they come from an unordered set of cardinality
M( j) . In this latter case, without loss of generality, we will assume that x ( j) ∈ I ( j) = {1, . . . , M( j) }.
When the label space Y is real-valued (for example, the label represents the mass of a particle),
the problem is known as regression, whereas when the labels come from a finite set of classes (for
example, Y = {signal, background}), we are talking about classification. The quality
of g on an
arbitrary pair (x, y) ∈ X × Y is measured by an error or loss function L g, (x, y) that depends
on the type of problem. In regression, the goal is to get g(x) as close to y as possible, so the loss
grows with the difference between g(x) and y. The most widely used loss function in this case is
the quadratic or squared loss
L2 g, (x, y) = g(x) − y .
In classification, typically there is no distance or similarity defined between the classes, so all we
can measure is whether or not g predicts correctly the class y. The usual loss in this case is the
one-loss or zero-one loss
LI g, (x, y) = I { g(x) 6= y},
where the indicator function I { A} is 1 if its argument A is true and 0 otherwise.
The goal of learning algorithms is not to memorize D , rather to generalize from it. Indeed,
it is rather trivial to construct a function g that has zero loss on every sample point (xi , yi ) by
explicitly setting g(xi ) , yi on all sample points from D , and, say, g(xi ) , 0 everywhere else.
We obviously have not learned anything from D , we have simply memorized it. It is also clear
that this function has very little chance to perform well on points not in the set D (unless 0 is a
good prediction everywhere in which case the problem itself is trivial). To formalize this intuitive
notion of generalization, it is usually assumed that all sample points (including those in D ) are
independent samples from a fixed but unknown distribution D, and the goal is to minimize the
expected loss (a.k.a., risk or error)
R( g) = E(x,y)∼D L g, (x, y) ,
where E(x,y)∼D { L} denotes the expectation of L with respect to the random variable (x, y) drawn
from the distribution D. When we know D, the optimal function is the one that minimizes the risk,
that is,
g∗ = arg min R( g).
With this notation, since the expectation of the indicator of an event is the probability of the
event, the misclassification probability P { g(x) 6= y} is the risk generated by the one-loss
RI ( g) = E(x,y)∼D LI g, (x, y) = P { g(x) 6= y}, 1
and so it can be shown that the optimal classifier (the so-called Bayes classifier) is
gI∗ (x) = arg max P {y | x}.
In regression, the risk is the expectation of the squared error
R2 ( g) = E(x,y)∼D ( g(x) − y)2 ,
and it can be shown easily that the optimal regressor is the conditional expectation of y given x,
that is,
g2∗ (x) = E {y | x}.
In practice, of course, the data distribution D is unknown, and the goal is to get close to
the performance of g∗ by only using the sample D . As our imaginary example demonstrates it,
finding a function g that minimizes the empirical risk (or training error)
b ( g) = 1
g, (xi , yi )
i =1
is not necessarily a good idea. Nevertheless, it turns out that the estimator
b ( g)
gb∗ = arg min R
can have good properties both in theory and in practice if the function class G is not too “rich”
so the functions in G are not too complex. In fact, the real error R( gb∗ ) of the empirically best
b ( gb∗ ), but the bias can be controlled,
classifier gb∗ is usually underestimated by the training error R
and the minimization (4) can lead to a good solution if the complexity of the function class G is
“small” compared to the number of data points n.
How to control the complexity of G and how to find an optimal function in G computationally
efficiently are the two main subjects of the design of supervised learning algorithms. Finding gb∗
in G is the subject of algorithmic design. The process is called training or learning, and the data
b ( gb) is minimized is the training set. Of course, once D is used for finding gb∗ ,
set D on which R
b ( gb∗ ) is no longer an unbiased estimator of R( gb∗ ).
it is tainted from a statistical point of view: R
Overfitting is the term that describes the situation when the risk R( gb∗ ) is significantly larger than
b ( gb∗ ). To detect and to assess overfitting, gb∗ has to be evaluated on a test set
the training error R
D , independent of D .
1 For denoting the risk generated by a particular loss, we will use the same index: the risk generated by the loss L will
be denoted by R• .
Parametric fitting and the maximum likelihood method
The main subject of the chapter is non-parametric (model-less) multi-variate classification, and
the setup of Section 1 is designed to formalize the theoretical framework for this approach. Nevertheless, it is worthy to note that “classical” low-dimensional fitting, be it maximum likelihood
or least-square, also fits into this framework. These methods are treated in other chapters, so here
we only sketch them to illuminate their relationship to the setup of Section 1.
When we know a generative probabilistic model, one of the possibilities of fitting a model is
to maximize the likelihood p(z). This can also be cast into the framework of this section with the
usual loss function
L P (z) = − log p(z),
and with the risk that L P implies
R P (z) = Ez∼D {− log p(z)}.
For example,
when the data points zi are triplets (xi , yi , σi ) with the generative model of yi ∼
N g(xi ), σi ,2 maximum likelihood reduces to weighted least square (“chi square”), that is,
g ( xi ) − y i
arg min ∑ − log p(xi , yi , σi ) = arg min ∑
i =1
i =1
Although the least-square method is mostly used in parametric fitting when G is a low-complexity
function class parametrized with a few parameters, neural networks and other model-less approaches can be used also with a (weighted) quadratic loss, and in this case all considerations
about overfitting, complexity regularization, and hyperparameter optimization, usually associated with non-parametric classification, are also valid.
Complexity regularization (model selection) and hyperparameter optimization
The simplest way to control the complexity of the function class G is to fix it either explicitly (for
example, by taking the set of all linear functions) or implicitly (for example, by taking the set
of all functions that a neural network with N neurons and one hidden layer can represent). In
parametric fitting, G is a set of functions parametrized with a low number of parameters, and the
dimensionality d of the input space is usually low. Most importantly, the number of parameters
is fixed independently of the size n of the training data set, so the overfitting issue only shows up
in goodness-of-fit tests (for example, in setting the degrees of freedom of the chi-square distribution).
The situation is different in non-parametric fitting. Contrary to what its name suggests, nonparametric fitting means that we have a lot of parameters. This approach is used when we have
little knowledge about the model that generated the observations, so we want to avoid the bias
caused by fitting a misspecified rigid model. More importantly, we do not want to decide the
complexity or the smoothness of the solution beforehand; we prefer that the data speak for itself.
Formally, we can define a (possibly infinite) set of function classes Gθ , parametrized by a vector θ of the so-called hyperparameters. Hyperparameters can take diverse forms. Most of them
are related to the complexity of the class; the number of neurons in a neural network, the depth
of decision trees, or the number of boosting iterations are typical examples. Other hyperparameters are penalty coefficients in a framework called complexity regularization [4] or structured risk
minimization [9]. In this setup, instead of finding the empirical minimizer in G , we penalize the
complexity of the functions g ∈ G by an appropriate penalty C ( g), and find
b ( g) + βC ( g),
gb∗β = arg min R
2 N ( µ, σ )
denotes the normal distribution with mean µ and standard deviation σ.
where β is a penalty coefficient (a hyperparameter) which has to be tuned. The classical practices
of weight decay or early stopping in neural networks fall in the category of structured risk minimization. Another typical example is the penalty coefficient (usually denoted by C) in support
vector machines. A third family of hyperparameters are simple “technical” parameters, such as
the learning rate in neural networks, or the number of features x ( j) tested at each cut in a decision
There is a vast literature on how to tune hyperparameters on the training set D itself. This socalled model selection problem has both Bayesian and frequentist solutions, but most of the time,
at least in model-less non-parametric fitting, optimality results are only valid asymptotically (as
training sample size n → ∞), and so they only provide some ballpark hints in practice. The solution adopted in practice is called validation. In the simplest setup, we choose gbθ∗ by minimizing
the training error in each set Gθ
b ( g; D),
gbθ∗ = arg min R
then we choose the overall best predictor gb∗ by minimizing the error on a held-out validation set
D 00
b ( gb∗ ; D 00 ),
gb∗ = arg min R
where we explicitly added D and D 00 as an argument of the error to emphasize that the two
minimizations use two different sets. Indeed, D 00 has to be independent of both the training set
D and the eventual test set D 0 (used for estimating the performance of the final classifier). This
procedure is known as hyperparameter optimization or hyperparameter tuning in machine learning.
The particularity of hyperparameter optimization is that the evaluation of the function f (θ ) =
b ( gb∗ ; D 00 ) is computationally expensive. Indeed, the evaluation of f (θ ) requires the optimization
of gbθ ∈ Gθ , that is, training gbθ on D and testing it on D 00 , which can take hours or days, depending
on the particular learning algorithm, the data size n, and the data dimensionality d. This feature relates hyperparameter optimization to experimental design where, say, we want to optimize
a handful parameters of a detector by evaluating its performance using expensive simulations.
When the number of hyperparameters is small (say, not more than 3), the usual procedure is
simple grid search: we discretize the components of θ, and evaluate f (θ ) for all members of the discrete set. For example, we train A DA B OOST for T ∈ {10, 20, 50, 100, 200, 500, 1000, 2000, 5000, 10 000}
iterations using decision trees with a number of leaves of N ∈ {2, 3, 5, 10, 20, 50, 100}, and then
select the best ( T, N ) pair out of the 70 trained functions. When the number of hyperparameters
exceeds three, this procedure rapidly becomes infeasible because the number of grid points blows
up exponentially. Simple heuristics, such as Latin hypercube search, simple random search [10],
or gradient search [11] can be applied, but the principled solution is surrogate optimization: we
replace f by a, usually non-parametric, smooth function which we train iteratively on a sequence
of evaluation points θt , f (θt ) . In each iteration t, a surrogate function fbt (θ ) is regressed over
the set θt0 , f (θt0 ) t0 =1 , then a new evaluation point is selected based on fbt (θ ). Gaussian process
regression [12] is one of the most popular concrete choices for the regression
method since it provides not only a regressor but also a conditional distribution p f (θ ) | θ , and so it can be used
with probabilistic criteria to select the next evaluation point θt . The best-known such criterion is
expected improvement. The recent success of deep neural networks [13, 14, 15], with often tens of
hyperparameters, generated a surge of papers on the subject [16, 10, 17, 18, 19], showing that the
jury is still out on what the best strategy is.
An example in low-dimensional fitting
To illustrate that overfitting and model selection do not only concern multi-variate classification,
we start by a simple example using Gaussian process (GP) regression [12] on a one-dimensional
fitting problem. The main ingredient of a GP is the kernel function K ( x, x 0 ) that defines the
smoothness of the regressor functions gb( x ). In this example we use the squared exponential
( x − x 0 )2
K ( x, x 0 ) = a exp −
where a and w are hyperparameters to be tuned. Without going into the details, the GP regression
function is given in the form of
gb( x ) =
∑ αi K(x, xi ).
i =1
The coefficient vector α = (α1 , . . . , αn ) is given by
α = (K + Σ)−1 y,
where K = [K ( xi , x j )]i,j
=1 is the Gram matrix and y = (y1 , . . . , yn ) is the label vector. The diagonal
matrix Σ contains either the squared uncertainties σi2 in the diagonal in case they are known, or
a constant σ2 which becomes a third hyperparameter that has to be tuned along with a and w.
There are both Bayesian and frequentist techniques to find the optimal hyperparameters a,
w, and σ using a single training set D . These methods work well in practice, so validation is
not a common technique in GP regression. Nevertheless, for the sake of illustrating the general
technique, we show on an example how validation works in this case. We generate 50 training
and 50 validation points ( xi , yi ) by first drawing xi uniformly from the interval [3, 100], and then
drawing yi ∼ N g( xi ), 0.4 , where
√ x
g( x ) = sin
is our target regression function. Figure 1 depicts g( x ) in blue, and the training set D in red
(the validation set D 00 is not shown). For the sake of simplicity, we fix a = 10 and σ = 0.4 and
only tune the kernel width w. The role of w is to set the smoothness of the regressor function.
We plot three cases in Figure 1. When w = 10, the estimated regressor gb( x ) clearly overfits
p data: it follows the training data too closely, achieving a root mean square error (RMSE)
R2 ( g) = 0.33 on the training set. At the same time, on the validation set its RMSE is 0.47,
giving a qualitative indication of overfitting. When w = 80, we are underfitting (oversmoothing)
the data. The training and validation RMSEs are close (0.47 and 0.48, respectively), but they are
both suboptimal. At the optimum w = 40, selected by the validation procedure, we can achieve
a 0.39 training RMSE and a 0.38 validation RMSE. The validation RMSE slightly underestimates
the expected RMSE since w was selected based on this sample, but this “hyper-overfitting” is
negligible. In fact it is comparable to overfitting a single-parameter function in a parametric
Convex losses in binary classification
Finding the empirical minimizer gb∗ (4), gbθ∗ (6), or gb∗β (5) can be algorithmically challenging even
in relatively “simple” function sets G . For example, finding the linear binary classifier that minibI ( g) is NP hard [20].3 One way to make the learning problem tractable
mizes the training error R
bI ( g) by defining convex losses that upper bound the one-loss
is to relax the minimization of R
LI g, (x, y) (1).
To formalize this, we need to introduce some notions used in binary classification. Most of
the learning algorithms do not directly output a {±1}-valued classifier g, rather, they learn a
real-valued discriminant function f , and obtain g by thresholding the output of f (x) at 0, that is,
+1 if f (x) > 0,
g (x) =
−1 if f (x) ≤ 0.
Figure 1: Over- and underfitting√
in non-parametric GP regression. The blue curve is the target
regression function g( x ) = sin
x . The red dots are the training set: xi is drawn uniformly
from [3, 100], and yi ∼ N g( xi ), 0.4 . Two of the GP hyperparameters are fixed to a = 10 and
σ = 0.4, and the third hyperparamater w is tuned on a validation set, yielding an optimal value
of w = 40.
Using the real-valued output of the discriminant function, the classifier can be evaluated on a
finer scale by defining the (functional) margin
ρ = ρ f , (x, y) = f (x)y.
With this notation, the misclassification indicator (one-loss) of a discriminant function f on a
sample point (x, y) can be re-defined as a function of the margin ρ by
LI f , (x, y) = LI ρ f , (x, y) = LI (ρ) = I {ρ < 0}.
Besides the sign of ρ that represents the classification error, the magnitude of ρ is also important: it
indicates the confidence or the robustness of the classification. Indeed, the combination of a large
ρ with a Lipschitz-type (slope) penalty on f means that the geometric margin ρ(g) is also large, that
is, x is far from the decision border (defined by the set of points for which f (x) = 0, see Figure 2).
The idea behind large margin classification is to design loss functions that “push” the decision
border away from the training points. The goal is not just to minimize the training zero-one error
RI ( g) (2) but also to increase the margin of the correctly classified points. The common feature
of these loss functions is that 1) they penalize larger errors (negative margins) more than smaller
ones, and 2) they keep penalizing even correctly classified points especially if they are close to
the decision border (their margin is close to zero). Formally, one can define smooth convex upper
f HxL = w x + w0
- Ρ2
f HxL
Figure 2: Geometric and functional margins. The labels of the red and blue points are y = +1 and
y = −1, respectively. (a) An upper bound ω on the slope w and a functional margin ρ implies
a geometric margin (distance from the point x : f (x) = 0) of ρ(g) > ρ/ω. (b) The discriminant
function f (x) and the induced classifier g(x). The blue point (with label y = −1) is misclassified
and the two red points (with label y = +1) are correctly classified, but the second red point has a
larger margin f ( x )y indicating that its classification is more robust.
bounds of the margin-based one-loss LI (ρ), and minimize the corresponding empirical risks inbI . The most common losses, depicted in Figure 3, are the exponential
stead of the training error R
L EXP (ρ) = exp(−ρ),
the hinge loss
L1+ (ρ) = max(0, 1 − ρ),
L LOG (ρ) = log2 exp(−ρ) + 1 .
and the logistic loss
Given the margin-based loss L• (ρ), the corresponding margin-based empirical risk can be defined as
b • ( f ) = 1 ∑ L • ( f ( xi ) y i )
n i =1
b EXP ( f ), R
b1+ ( f ) , or R
b LOG ( f ) have both algorithMinimizing the margin-based empirical risks R
mic and statistical advantages. First, combining the minimization of these risks with Lipschitztype penalties4 often leads to convex optimization problems that can be solved with standard
algorithms (e.g., linear, quadratic, or convex programming). Second, it can also be shown within
the complexity regularization framework that the minimization of these penalized convex risks
leads to large margin classifiers with good generalization properties, confirming the intuitive
explanation of the previous paragraph.
Binary classification algorithms
The three most popular binary classification algorithms are the multi-layer perceptron or neural
network (NN) [21, 1], the support vector machine (SVM) [22, 23, 9], and A DA B OOST [24]. They have
very different origins, and the algorithmic details also make them stand apart, nevertheless, they
4 often
of the form of L1 or L2 penalties on the coefficient vector of a generalized linear model; see Section 4
exponential HAdaBoostL
hinge HSVML
logistic HNNL
Figure 3: Convex margin losses used in binary classification.
share some important concepts, and their practical success is explained, at least qualitatively, by
the same theory on large margin classification.
An important pragmatic similarity is that they all learn generalized linear discriminant functions of the form
∑ α ( t ) h ( t ) (x).
f (x) =
t =1
In neural networks h(t) (x) is a perceptron, that is, a linear combination of the input features followed by a sigmoidal nonlinearity σ (such as arctan)
h ( t ) (x) = σ
w0 +
∑ wj
(t) ( j)
j =1
where x ( j) is the jth component of the d-dimensional input vector x. The output weight vector
α = α(1) , . . . , α(T ) and the T × (d + 1)-dimensional input weight matrix
 0.
 ..
(T )
.. 
. 
(T )
are learned using gradient descent optimization (called back-propagation in the NN literature). In
the case of binary classification the neural network is usually trained to minimize the logistic loss
(9). One of the advantages of NNs is their versatility: as long as the loss function is differentiable,
it can be plugged into the algorithm. The differentiability condition imposed by the gradient
descent optimization, on the other hand, constrains the loss function and the nonlinearities used
in the hidden units: one could not use, say, a Heaviside-type nonlinearity or the one-loss.
The invention of the multilayer perceptron in 1986 [21] was a technological breakthrough:
complex pattern recognition problems (e.g., handwritten character recognition) could suddenly
be solved efficiently using neural networks. At the same time, the theory of machine learning
at these early days was not yet developed to the point of being able to explain the principles
behind algorithmic design. Most of the engineering techniques (such as weight decay or early
stopping) were developed using a trial-and-error approach, and they were theoretically justified
only much later within the complexity regularization and large margin framework [25]. Partly
because of the “empirical” nature of neural network design, a common belief developed about
the “user-unfriendliness” of neural networks. Added to this reputation was the uneasiness of
having a non-convex optimization problem: back-propagation cannot be guaranteed to converge
to a global minimum of the empirical risk. This is basically a no-issue in practice (especially on
today’s large problems), still, it is a common criticism from people who usually have never experimented with neural networks on real problems. As a consequence, neural networks were
slightly over-shadowed in the 90s with the appearance of the support vector machine and A D A B OOST . Today neural networks are, again, becoming more popular partly because of userfriendly program packages (e.g., [26]), partly due to the computational efficiency of the training
algorithm (especially its stochastic version), and partly because of the recent success of unsupervised feature-learning techniques that use deep neural architectures to solve hard computer
vision and language processing problems [13, 14, 15].
Support vector machines also learn generalized linear discriminant functions of the form (11)
h ( t ) (x ) = y t K (xt , x ),
where K (x, x0 ) is a positive semidefinite kernel function that expresses the similarity between two
observations x and x0 . K (x, x0 ) is usually monotonically decreasing with the distance between
x and x0 . As in Gaussian process regression (Section 2.1), the most common choice for K is the
squared exponential (a.k.a. Gaussian) kernel. The index t ranges over t = 1, . . . , n which means
that each training point (xt , yt ) ∈ D contributes to f a kernel function centered at xt with a
sign equal to the label yt , so the final discriminant function can be interpreted as a weighted
nearest neighbor classifier where the weight comprises of the “similarity term” K (xt , x) and the
“importance term” α(t) .
The objective of training the SVM is to find the weight vector α = α(1) , . . . , α(T ) that minimizes the hinge loss (8) with a complexity penalty (the L2 loss on the weights of features in the
Hilbert space induced by K (x, x0 )). The objective function was explicitly designed based on the
theory of large margin classification to ensure good generalization properties. A great advantage
of the setup is that the objective is a quadratic function of α with linear constraints 0 ≤ α(t) ≤ C
(the so-called box constraints), so the global optimum can be found using quadratic programming. The result is sparse: only a subset of the training points in D have nonzero coefficients α(t) .
These points are called support vectors, giving the name of the method.
The biggest disadvantage of the technique is its training time: naı̈ve quadratic programming
solvers run in super-quadratic time, and even with the most sophisticated tricks it is hard to beat
the O(n2 ) barrier. The second disadvantage of the method is that in high-dimensional problems
the number of support vectors is comparable to the size of the training set5 , so, when evaluating f at the test phase is a bottleneck (see Section 6.1), SVMs can be prohibitively slow. Third,
unlike neural networks, SVMs are designed for binary classification, and the generic extensions
for multi-class classification and regression do not reproduce the performance of binary SVM.
Despite these disadvantages, the appearance of the support vector machine in the middle of the
90s revolutionized the technology of pattern recognition. Besides its remarkable generalization
performance, the small number of hyperparameters6 and the appearance of turn-key software
packages7 made SVMs the method of choice for a wide range of applications involving small-tomoderate size training sets. With the appearance of extra-large training sets in the last decade,
training time and optimization became more important issues than generalization [27], so SVMs
lost somewhat their dominant role.
5 This
loss of sparsity is one of the manifestations of the phenomenon known as the curse of dimensionality.
in the bound of the αs and a length scale parameter of the kernel function K (x, x0 )
7,∼cjlin/libsvm,, http:
A DA B OOST learns a generalized linear discriminant function of the form (11) in an iterative
fashion. It can be considered as constructing a neural network by adding one neuron at a time,
but since back-propagation is no longer applied, there is no differentiability restriction on the
base classifiers h(t) . This opens the door to using domain-dependent features, making A DA B OOST
easy to adapt to a wide range of applications. The basic binary classification algorithm (Figure 4)
consists of elementary steps that can be implemented by a first year computer science student in
an hour. The only tricky step is the implementation of the base learner (line 3) whose goal is to
return h(t) with a small weighted error
bI (h, w) = 1
∑ w i I { h ( xi ) 6 = y i } ,
i =1
but most of the simple learning algorithms (e.g., decision trees, linear classifiers) can be easily
adapted to this modified risk. The weights w = (w1 , . . . , wn ) over the training points are initialized uniformly (line 1), and then updated in each iteration by a simple and intuitive rule
(lines 7-10): if h(t) misclassifies (xi , yi ), the weight wi increases (line 8), whereas if h(t) correctly
classifies (xi , yi ), the weight wi decreases (line 10). In this way subsequent base classifiers will
concentrate on points that were “missed” by previous base classifiers. The coefficients α(t) are
also set analytically to the formula in line 5 which is monotonically decreasing with respect to
the weighted error.
A DA B OOST D , B ASE (·, ·), T
1 w(1) ← (1/n, . . . , 1/n)
. initial weights
2 for t ← 1 to T
h(t) ← B ASE D , w(t)
. base classifier
e ( t ) ← ∑ w i I h ( t ) ( xi ) 6 = y i
. weighted error of the base clas-
i =1
1 − e(t)
← ln
for i ← 1 to n
h ( t ) (x
. coefficient of the base classifier
. re-weighting the training points
6= yi then
( t +1)
. error
. weight increases
. correct classification
( t +1)
11 return f (T ) (·) =
2 (1− e ( t ) )
. weight decreases
∑ α(t) h(t) (·)
. weighted “vote” of base classifiers
t =1
Figure 4: The pseudocode of binary A DA B OOST. D = (x1 , y1 ), . . . , (xn , yn ) is the training set,
B ASE (·, ·) is the base learner, and T is the number of iterations.
This simple algorithm has several beautiful mathematical properties. First it can be shown
[24] that if each base classifier h(t) is slightly better than a random guess (that is, every weighted
bI of the final discriminant
error e(t) ≤ 12 − δ with δ > 0), then the (unweighted) training error R
function f (T ) becomes zero after at most
log n
steps. The logarithmic dependence of T on the data size n implies that the technique is formally
a boosting algorithm from which the second part of its name derives.8 Second, it can be shown
b EXP ( f ) (7) using a greedy functional gradient
that the algorithm minimizes the exponential risk R
approach [28, 29]. In this alternative but equivalent formulation, in each iteration h(t) is selected
b EXP f (t−1) + αh(t) at α = 0, and then α(t) is set to
to maximize the decrease (derivative) of R
b EXP f (t−1) + αh(t) .
α(t) = arg min R
Furthermore, for inseparable data sets (for which no linear combination of base classifiers achieves
b EXP ( f (T ) ) goes to the minimum achievable exponential risk
0 training error), the exponential risk R
as T → ∞ [30]. It can also be proven [31] that for the normalized discriminant function
∑ T α ( t ) h ( t ) (x)
fe(x) = t=1 T
∑ t =1 α ( t )
the margin-based training error
b (θ ) ( fe) = 1
fe(xi )yi < θ
i =1
also goes to zero exponentially fast for all θ < ρe∗ /2, where
ρe∗ =
α:∑tT=1 |e
αt |=1
min ∑ e
α ( t ) h ( t ) ( xi ) y i
t =1
is the maximum achievable (normalized) minimum margin. This results shows that A DA B OOST,
similarly to the support vector machine, leads to a large margin classifier. It also explains the
surprising experimental observation that the generalization error RI ( f ) (estimated on a test set)
bI ( f ) reaches 0.
often decreases even after the training error R
A DA B OOST arrived to the pattern recognition scene in the late 90s when support vector machines were in full bloom. A DA B OOST has a lot of advantages over its main rival: it is fast (its
time complexity is linear in n and T), it is an any-time algorithm9 , it has few hyperparameters, it
is resistant to overfitting, and it can be directly extended to multi-class classification [32]. Due to
these advantages, it rapidly became the method of choice of machine learning experts in certain
types of problems where the natural setup is to define a large set of plausible features of which
the final solution f (T ) uses only a few (a so called sparse classifier). Arguably the most famous
application is the first face detector running real-time on 30 frame-per-second video [33]. At the
same time, A DA B OOST is much less known among users with no computer science background
than the support vector machine. The reason is paradoxically the simplicity of A DA B OOST: since
it is so easy to implement with a little background in programming, no machine learning expert
took the effort to provide a turn-key software package easily usable for non-experts.10 In fact
it is not surprising that the only large non-computer-scientist community in which A DA B OOST
is much more popular than SVM is experimental physics: physicists, especially in high energy
physics, are heavy computer-users with considerable programming skills. In the last five years
algorithm is also A DAptive because the coefficients α(t) depend on the errors e(t) of the base classifiers h(t) .
outputs a classifier even if it is stopped before convergence.
10 We are attempting to improve the situation with our free multi-class program package available at http://
8 The
9 It
The regression problem can be illustrated by our recent work [45] in which we aim to predict
the number of muons (an elementary particle) in a signal recorded in a water Cherenkov detector
of te Pierre Auger experiment [46] (see Chapter 4 for a detailed description of the context). The
pipeline, again, starts by extracting features that are plausibly correlated with the muon content
of the signal. The 172 features are quite correlated, so first we apply principal component analysis
to reduce the size of the observation vector x to 19. Then we train a neural network [47, 48] and
convert its output into a point estimate and a pointwise error bar (uncertainty) of the number of
In [49, 50] we use A DA B OOST.MH for web page ranking. The input x of the classifier g is a set
(and other asimilar
seem and
to have
been �taking
over the
the relevance
field of
query and
a document,
the label
of the document to the query. The goal is to rank the documents in order of their relevance. Of
by ensemble-based
SVM isisalmost
in the
the most
if the relevance
of the and
will also be
DA B OOST (Figure 4) is indeed simple to implement, multi-class A DA B OOST
good. Nevertheless, it is hard to formally derive a meaningful loss for each (document, query,
has some nontrivial
There areloss
the original
triplet from
the particular
on rankings.
The of
to thisbinary
is a
ADpost-processing technique called calibration: instead of directly using the output of the classifier
A B OOST.M1 and A DA B OOST.M2 algorithms [24] are largely suboptimal compared to A DA B OOST.MH
g, we send it through another regressor
or ranker which is now trained on the desired loss funcpublished a couple of years later [32].11 On the other hand, the A DA B OOST.MH paper [32] did
tion. Our concrete system, descibed in detail in Section 3.8, also contains another postprocessing
not specify the implementation details of multi-class base learning, making the implementation
modul called model aggregation: instead of training one classifier g, we train a large number of
non-trivial. For more information on multi-class A DA B OOST.MH we refer the reader to the docclassifiers by varying data preprocessing and algorithmic hyperparameters, and combine the reumentation of M ULTI B OOST, available from the website.
sults using a simple weighted voting scheme.
Trigger design in experimental physics
In this section we illustrate the versatility of the abstract supervised learning model described in
Our last example is a work in progress; we present it here because it is one of those problems for
this chapter through presenting some of the machine learning applications we have worked on.
which no turn-key machine learning solution exists even though the problem definitely belongs
It turns out that real-world applications are never so simple as just taking a turn-key implementotation
the domain
of box
is trigger
in experimental
out of the
and running
it. The
To make
a system
one needs
both domainphysics.
and machine learning expertise, and so it often requires an interdisciplinary approach and
siderable communication effort from experts of different backgrounds. Nevertheless, onceeither
rays) setup
or they
can be in
is reduced
the abstract
Section 1,by
A common feature of today’s detectors is that the phenomena they want
be veryaccelerators).
to measure
that it is
As the examples willwhich
regression isdifficult
only onetostep
in the signal
nition pipeline (Figure 5). Data collection is arguably the most costly step in most of the like
cations. In particular, harvesting the labels yi usually involves human interaction. How to do
to collect a large
of observations
that, withto
this in aiscost-effective
way statistics
by, for example,
using CAPTCHAs
[36] or
the targeted
the abackground
is isoften
of magnitudes
people label
images bySince
game [37, 38],
a challenging
subject. more
part of physics
the background/signal
be done
offline. Due
1 insignal,
the otherthan
simulators can beseparation
used to sample
the distribution
capacity ordata
raw signal has to be discarded
x | y), limited
so in these
is usually
by online triggers.
Multivariate discrimination or classification is one of the most-studied problem in machine learning, learning
with a plethora
of well-tested
In the machine
a trigger is just a binary classifier with
Data collection
2 The supervised learning setup
There are several attributes that make the trigger a special binary classifier. First, it is extremely
goal of supervised
to infer a function
: Xcases.
→ LThis
a datathe
set clasunbalanced: the
1 in a lotg of
� it is very nhard to beat the
sical setup of minimizing the error probability
D = R(Ix(1g,)y(3.2)
1 ), . . . , (xn , yn ) ∈ (X × L)
trivial constant classifier g(x) ≡ BACKGROUND which has an error of RI ( g) = P(� = SIGNAL ).
Indeed, the natural
gain inoftrigger
is the true
indicatorx ( j) of the d-dimensional feature or
labels. or
� either real numbers or they come from an unordered set of cardinality
observation vector
x are
GTP g, (x, �) = I { g(x) = SIGNAL | � = SIGNAL }.
M( j) . In this
latter case, without loss of generality, we will assume that x ( j) ∈ I ( j) = {1, . . . , M( j) }.
When the label space L is real-valued, the problem is known as regression, whereas when
the labels come Postprocessing
from a finite set of classes,� we are �talking about classification. The quality of g
is measured by an error or loss function L g, (x, y) that depends on the type of problem. In
regression, the
to get
g(x) as
close to ypipeline.
as possible, so the loss grows with the difference
between g(x) and y. The most widely used loss function in this case is the quadratic or squared
� is a wide
�2 range of techniques
The secondloss
step of the pipeline is preprocessing �the data.� There
L2 of
g, (simplifying
x, y) = g(the
x) −supervised
y .
that can be applied here, usually with the objective
learning task.
First, if the observations
are not
in aisformat
of a real
vector x ∈
Rd , features
In classification,
no distance
or similarity
the classes, so all we
can measure is whether or not g predicts correctly the class y. The usual loss in this case is the
11 The commonly
used WEKA package (, for example, only contains a badly
losseffectively harming�the reputation
written implementation
of A
� of boosting as a whole.
LI g, (x, y) = I { g(x) �= y},
where the indicator function I { A} is 1 if its argument A is true and 0 otherwise.
13 is not to memorize D , rather to generalize from it. Indeed,
The goal of learning algorithms
it is rather trivial to construct a function g that has zero loss on every sample point (xi , yi ) by
explicitly setting g(xi ) � yi on all sample points from D , and, say, g(xi ) � 0 everywhere else.
We obviously have not learned anything from D , we have simply memorized it. It is also clear
that this function has very little chance to perform well on points not in the set D (unless 0 is a
( j)
( j)
vectors ( x1 , . . . , xn ) of the data matrix) should be extracted from the raw data. The goal here
is to find features that are plausibly correlated with the label y, so domain knowledge is almost
always necessary to carry out this step. Since learning algorithms usually deteriorate as the
dimension of the input x increases (because of the so called curse of dimensionality), dimensionality
reduction is often part of data preprocessing. Principal component analysis is the basic tool that
is usually applied, but nonlinear manifold algorithms [39, 40] may also be useful if the training
set is not too large. The output space Y can also be transformed to massage the original problem
into a setup that can be solved by standard machine learning tools. This can be done in an ad
hoc way, but there exist also principled reduction techniques between machine learning problems
(binary/multi-class classification, regression, even reinforcement learning) [41].
After training, postprocessing the results can also be an important step. If, for example, the
original complex problem was reduced to an easy-to-solve setup, re-transforming the obtained
labels and even re-calibrating the solution is often a good idea. In well-documented challenges
of the last five years [34, 42, 35] we have also learned that the most competitive results are obtained when divers models trained using different algorithms are combined, so model aggregation
techniques are also becoming part of the generic machine learning toolbox.
Sometimes it happens that an application poses a specific problem that can not be solved
with existing tools. Solving such a problem and generalizing the solution to make it applicable
for similar problems is one of the motivational engines behind the development of the machine
learning domain.
Music classification, web page ranking, muon counting
In [43] we use A DA B OOST for telling apart speech from music. The system starts by constructing
the spectrogram on a set of recorded audio signals which constitute the observation vectors x
in this case. A DA B OOST is then run in a feature space inspired by image classification on the
spectrogram “images”. The output y of the system is binary, that is, y ∈ Y = { MUSIC, SPEECH }.
In [44] we stay within the music classification domain but we tackle a more difficult problem:
finding the performing artist and the genre of a song based on the audio signal of the first 30 s of
the song. The first module of the classifier pipeline is an elaborate signal processor that collects
a vector of features for each 2 s segment and then aggregates them to constitute an observation
vector x with about 800 components per song. We train two systems, one for finding the artist
performing the song and another for predicting its genre. This application also stretches the limits
of the classical multi-class (single-label) setup. It is plausible that a song belongs to several genres
leading to a so-called multi-label classification. It may also be useful to train a unique system for
predicting both the artist and the genre at the same time. This problem is known as multi-task
The multi-variate regression problem can be illustrated by our recent work [45] in which we
aim to predict the number of muons in a signal recorded in a water Cherenkov detector of the
Pierre Auger experiment [46]. The pipeline, again, starts by extracting features that are plausibly
correlated with the muon content of the signal. The 172 features are quite correlated, so first we
apply principal component analysis to reduce the size of the observation vector x to 19. Then we
train a neural network [1, 26] and convert its output into a point estimate and a pointwise error
bar (uncertainty) of the number of muons.
In [47, 48] we use A DA B OOST.MH for web page ranking. The input x of the classifier g is a set
of features representing a search query and a document, and the label y represents the relevance
of the document to the query. The goal is to rank the documents in order of their relevance. Of
course, if the relevance of the document is correctly predicted, the implied ranking will also be
good. Nevertheless, it is hard to formally derive a meaningful loss for each (document, query,
relevance) triplet from the particular loss defined on rankings. The solution to this problem is a
post-processing technique called calibration: instead of directly using the output of the classifier
g, we send it through another regressor or ranker which is now trained to minimize the desired
risk. Our concrete system also contains another postprocessing module called model aggregation:
instead of training one classifier g, we train a large number of classifiers by varying data preprocessing and algorithmic hyperparameters, and combine the results using a simple weighted
voting scheme.
Three open research problems
In this section we briefly describe three open research problems that are either relevant to or
even motivated by certain unorthodox applications of multivariate discrimination in experimental physics.
Trigger design: classification with test-time constraints
One of the recent applications of multivariate discriminants in high-energy physics is trigger
design [49]. The goal of a trigger is to separate signals generated by a phenomenon to be detected
or measured from background, which is an observed event that just happen to look like real signal
because of random fluctuations or due to some uninteresting phenomena. The final goal of the
analysis is to collect a large statistics of observations that, with high probability, were generated
by the targeted phenomenon. Since the background is often several orders of magnitudes more
frequent than the signal, part of the background/signal separation cannot be done offline. Due
to either limited disk capacity or limited bandwidth, most of the raw signal has to be discarded
by online triggers.
In the machine learning paradigm, a trigger is just a binary classifier with
There are several attributes that make the trigger a special binary classifier. First, it is extremely
unbalanced: the probability of BACKGROUND is practically 1 in a lot of cases. This makes the
classical setup of minimizing the error probability RI ( g) (2) inadequate: it is very hard to beat the
trivial constant classifier g(x) ≡ BACKGROUND which has an error of RI ( g) = P(y = SIGNAL ).
Indeed, the natural gain in trigger design is the true positive or hit indicator
GTP ( g, (x, y)) = I { g(x) = SIGNAL | y = SIGNAL }.
Taking the complement of the true positive indicator
L TP ( g, (x, y)) = 1 − I { g(x) = SIGNAL | y = SIGNAL } = I { g(x) = BACKGROUND | y = SIGNAL }
as the loss, the implied risk is
R TP ( g) = E(x,y)∼D { L TP ( g, (x, y))} = P { g(x) = BACKGROUND | y = SIGNAL }.
Again, minimizing R TP ( g) is trivial by setting g(x) ≡ SIGNAL this time, so the goal is to minimize
R TP ( g) with a constraint that the false positive rate
R FP ( g) = P( g(x) = SIGNAL | y = BACKGROUND )
is kept below a fixed level p FP . As we mentioned above, in triggers we have P(y = SIGNAL ) P(y = BACKGROUND ), so the false positive rate R FP ( g) is approximately equal to the unconditional positive rate P( g(x) = SIGNAL ). In experimental physics terminology this means that a
constraint is imposed on the trigger rate.
The second attribute that makes trigger design special is that we have strict computational
constraints imposed on the evaluation of the classifier g on test samples x. Typically, observations
x arrive at a given rate and g(x) has to be run online. The designer can have some flexibility on
the parallel handling of the incoming events, but computational resources are often limited. In
certain cases12 the electric consumption may also be limited and harsh conditions may require
the use of robust hardware with low clock-rate.
Trigger design shares these two attributes (unbalanced classes and test-time constraints) with
object detection in computer vision where machine learning has been applied widely. For example, when the goal is to detect faces in images, the probability of the signal class (face) is much
lower then the probability of background (everything else). We have also computational constraints at test time if the goal is to detect faces online in video recordings with given frame rate
and the detector hardware must fit into a compact camera. What makes trigger design slightly
more challenging is the sometimes extremely low signal probability and the fact that the computational cost of each feature (components of x) can vary in a large range. For example, the LHCb
trigger [51, 49] can use “cheap” observables that can be evaluated fast to rapidly obtain a rough
classifier. One can also almost reconstruct the collision event which can take up a large portion
of the allotted time, but the resulting feature can be used reliably to discard background events.
This example shows why a natural answer to these challenges is to design cascade classifiers
both in experimental physics and in object detection [33, 52, 53, 54, 55, 56]. A cascade classifier
g(x) is composed of a list of simpler binary classifiers h1 (x), . . . , h N (x) evaluated sequentially.
For j < N, if h j (x) classifies the observation x negatively, the final classification of g(x) is BACK GROUND , and if the output of h j (x) is positive, the observation is sent to the next ( j + 1)th stage
(or level in the terminology of experimental physics). The classifier h N (x) at the last stage is the
only one that can classify x as a SIGNAL. In computer vision, the stage classifiers h j (x) are usually
learned using classification algorithms (most often A DA B OOST). Some of the newest detection
algorithms also attempt to learn the cascade structure automatically, nevertheless, manual experimentation is usually required to set hyperparameters (stagewise false positive/false negative
rates, computational complexity of stage classifiers h j (x)). In [57], we present a principled approach that can be used to automatically design test-time constrained classifiers. The automatic
design also allows us to go beyond the cascade structure which is, although quite intuitive, an
artificial constraint to keep the classifier structure simple and to accommodate manual tuning.
Learning to discover
The main application of multivariate discrimination in high-energy particle physics is, interestingly, not classification. The goal in these applications is to increase the sensitivity of countingbased statistical tests [58, 59]. Intuitively, the goal is to find regions of the feature space where
the signal is present or where it is amplified with respect to its average abundance. Once the
subregion is found, we claim the discovery of a novel phenomenon (particle) when the number of events in the region is significantly higher than that predicted by the pure background
hypothesis. The simplest formal goal, motivated by a Poisson test, is to maximize
G( f ) = √
∑ I { f (xi ) = SIGNAL ∧ yi = SIGNAL}
i =1
∑ I { f (xi ) = SIGNAL ∧ yi = BACKGROUND}.
i =1
Although G ( f ) has the right “flavor”, it does not take into consideration the statistical fluctuations of the test when s and/or b are small, so other, more sophisticated, approximate criteria
have also been derived [60]. Whereas all these criteria (including (12)) are clearly related to the
classical classification error RI ( f ), the two are not equivalent. A notable difference is that the
expectation of G depends on the sample size n, whereas increasing sample size only decreases the
12 For
example, in JEM EUSO [50], where the detector will be installed on the International Space Station.
variance of RI . Nevertheless, the standard practice is to learn a discriminant function f : Rd → R
on D using standard classification methods that minimize the classification error R, and then use
G only to optimize a threshold θ which defines the function gθ by
if f (x) > θ
gθ (x) =
This way of using multivariate discriminants raises several interesting research questions. First,
given a concrete test, what should the training criteria G be? Second, what is the relationship between G and RI ( f )? Finally, how to adapt classical classification algorithms to the given criteria?
Deep learning for automatic feature construction
Finally let us raise a completely open research question. The current technology of multivariate
discrimination allows us to classify objects that are represented by vectors x = x (1) , . . . , x (d) of
( j)
fixed length d. The representation has to be “semantically aligned”, that is, x1 must be compa( j)
rable to x2 in two objects x1 and x2 . At the same time, raw observation, be it pixels of an image
or electronic channels in a detector event, seldom comes in this form. Today, the process of translating the raw observation into a semantically aligned vector of features is in the hands of the domain expert. One of the most important movements of the last decade in machine learning is the
development of a family of techniques for building deep unsupervised neural architectures [13].
The goal is to construct representations in a mostly non-supervised way that can capture deep
invariances in the raw data. Although the field is rather young, it already had a major impact on
natural language processing [14] and computer vision [15]. Interesting and natural questions are
whether these techniques can be adapted to scientific data, whether the invariances in, say, raw
detector data are sufficient to construct representations that could be useful in the ultimate analysis, and whether automatic or semi-automatic feature extraction can improve on the manually
constructed representations.
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