# Linear Regression

Machine learning is broadly split into two camps, statistical learning and non-statistical learning. The latter we’ve started to get a good picture of on this blog; we approached Perceptrons, decision trees, and neural networks from a non-statistical perspective. And generally “statistical” learning is just that, a perspective. Data is phrased in terms of independent and dependent variables, and statistical techniques are leveraged against the data. In this post we’ll focus on the simplest example of this, linear regression, and in the sequel see it applied to various learning problems.

As usual, all of the code presented in this post is available on this blog’s Github page.

## The Linear Model, in Two Variables

And so given a data set we start by splitting it into independent variables and dependent variables. For this section, we’ll focus on the case of two variables, $X, Y$. Here, if we want to be completely formal, $X,Y$ are real-valued random variables on the same probability space (see our primer on probability theory to keep up with this sort of terminology, but we won’t rely on it heavily in this post), and we choose one of them, say $X$, to be the independent variable and the other, say $Y$, to be the dependent variable. All that means in is that we are assuming there is a relationship between $X$ and $Y$, and that we intend to use the value of $X$ to predict the value of $Y$. Perhaps a more computer-sciencey terminology would be to call the variables features and have input features and output features, but we will try to stick to the statistical terminology.

As a quick example, our sample space might be the set of all people, $X$ could be age, and $Y$ could be height. Then by calling age “independent,” we’re asserting that we’re trying to use age to predict height.

One of the strongest mainstays of statistics is the linear model. That is, when there aren’t any known relationships among the observed data, the simplest possible relationship one could discover is a linear one. A change in $X$ corresponds to a proportional change in $Y$, and so one could hope there exist constants $a,b$ so that (as random variables) $Y = aX + b$.  If this were the case then we could just draw many pairs of sample values of $X$ and $Y$, and try to estimate the value of $a$ and $b$.

If the data actually lies on a line, then two sample points will be enough to get a perfect prediction. Of course, nothing is exact outside of mathematics. And so if we were to use data coming from the real world, and even if we were to somehow produce some constants $a, b$, our “predictor” would almost always be off by a bit. In the diagram below, where it’s clear that the relationship between the variables is linear, only a small fraction of the data points appear to lie on the line itself.

An example of a linear model for a set of points (credit Wikipedia).

In such scenarios it would be hopelessly foolish to wish for a perfect predictor, and so instead we wish to summarize the trends in the data using a simple description mechanism. In this case, that mechanism is a line. Now the computation required to find the “best” coefficients of the line is quite straightforward once we pick a suitable notion of what “best” means.

Now suppose that we call our (presently unknown) prediction function $\hat{f}$. We often call the function we’re producing as a result of our learning algorithm the hypothesis, but in this case we’ll stick to calling it a prediction function. If we’re given a data point $(x,y)$ where $x$ is a value of $X$ and $y$ of $Y$, then the error of our predictor on this example is $|y - \hat{f}(x)|$. Geometrically this is the vertical distance from the actual $y$ value to our prediction for the same $x$, and so we’d like to minimize this error. Indeed, we’d like to minimize the sum of all the errors of our linear predictor over all data points we see. We’ll describe this in more detail momentarily.

The word “minimize” might evoke long suppressed memories of torturous Calculus lessons, and indeed we will use elementary Calculus to find the optimal linear predictor. But one small catch is that our error function, being an absolute value, is not differentiable! To mend this we observe that minimizing the absolute value of a number is the same as minimizing the square of a number. In fact, $|x| = \sqrt(x^2)$, and the square root function and its inverse are both increasing functions; they preserve minima of sets of nonnegative numbers.  So we can describe our error as $(y - \hat{f}(x))^2$, and use calculus to our heart’s content.

To explicitly formalize the problem, given a set of data points $(x_i, y_i)_{i=1}^n$ and a potential prediction line $\hat{f}(x) = ax + b$, we define the error of $\hat{f}$ on the examples to be

$\displaystyle S(a,b) = \sum_{i=1}^n (y_i - \hat{f}(x_i))^2$

Which can also be written as

$\displaystyle S(a,b) = \sum_{i=1}^n (y_i - ax_i - b)^2$

Note that since we’re fixing our data sample, the function $S$ is purely a function of the variables $a,b$. Now we want to minimize this quantity with respect to $a,b$, so we can take a gradient,

$\displaystyle \frac{\partial S}{\partial a} = -2 \sum_{i=1}^n (y_i - ax_i - b) x_i$

$\displaystyle \frac{\partial S}{\partial b} = -2 \sum_{i=1}^n (y_i -ax_i - b)$

and set them simultaneously equal to zero. In the first we solve for $b$:

$\displaystyle 0 = -2 \sum_{i=1}^n y_i - ax_i - b = -2 \left (-nb + \sum_{i=1}^n y_i - ax_i \right )$

$\displaystyle b = \frac{1}{n} \sum_{i=1}^n y_i - ax_i$

If we denote by $x_{\textup{avg}} = \frac{1}{n} \sum_i x_i$ this is just $b = y_{\textup{avg}} - ax_{\textup{avg}}$. Substituting $b$ into the other equation we get

$\displaystyle -2 \sum_{i=1}^n (y_ix_i - ax_i^2 - y_{\textup{avg}}x_i - ax_{\textup{avg}}x_i ) = 0$

Which, by factoring out $a$, further simplifies to

$\displaystyle 0 = \sum_{i=1}^n y_ix_i - y_{\textup{avg}}x_i - a \sum_{i=1}^n (x_i^2 - x_{\textup{avg}}x_i)$

And so

$\displaystyle a = \frac{\sum_{i=1}^n (y_i - y_{\textup{avg}})x_i }{\sum_{i=1}^n(x_i - x_{\textup{avg}})x_i}$

And it’s not hard to see (by taking second partials, if you wish) that this corresponds to a minimum of the error function. This closed form gives us an immediate algorithm to compute the optimal linear estimator. In Python,

avg = lambda L: 1.0* sum(L)/len(L)

def bestLinearEstimator(points):
xAvg, yAvg = map(avg, zip(*points))

aNum = 0
for (x,y) in points:
aNum += (y - yAvg) * x
aDenom += (x - xAvg) * x

b = yAvg - a * xAvg
return (a, b), lambda x: a*x + b


and a quick example of its use on synthetic data points:

>>> import random
>>> a = 0.5
>>> b = 7.0
>>> points = [(x, a*x + b + (random.random() * 0.4 - 0.2)) for x in [random.random() * 10 for _ in range(100)]]
>>> bestLinearEstimator(points)[0]
(0.49649543577814137, 6.993035962110321)

## Many Variables and Matrix Form

If we take those two variables $x,y$ and tinker with them a bit, we can represent the solution to our regression problem in a different (a priori strange) way in terms of matrix multiplication.

First, we’ll transform the prediction function into matrixy style. We add in an extra variable $x_0$ which we force to be 1, and then we can write our prediction line in a vector form as $\mathbf{w} = (a,b)$. What is the benefit of such an awkward maneuver? It allows us to write the evaluation of our prediction function as a dot product

$\displaystyle \hat{f}(x_0, x) = \left \langle (x_0, x), (b, a) \right \rangle = x_0b + ax = ax + b$

Now the notation is starting to get quite ugly, so let’s rename the coefficients of our line $\mathbf{w} = (w_0, w_1)$, and the coefficients of the input data $\mathbf{x} = (x_0, x_1)$. The output is still $y$. Here we understand implicitly that the indices line up: if $w_0$ is the constant term, then that makes $x_0 = 1$ our extra variable (often called a bias variable by statistically minded folks), and $x_1$ is the linear term with coefficient $w_1$. Now we can just write the prediction function as

$\hat{f}(\mathbf{x}) = \left \langle \mathbf{w}, \mathbf{x} \right \rangle$

We still haven’t really seen the benefit of this vector notation (and we won’t see it’s true power until we extend this to kernel ridge regression in the next post), but we do have at least one additional notational convenience: we can add arbitrarily many input variables without changing our notation.

If we expand our horizons to think of the random variable $Y$ depending on the $n$ random variables $X_1, \dots, X_n$, then our data will come in tuples of the form $(\mathbf{x}, y) = ((x_0, x_1, \dots, x_n), y)$, where again the $x_0$ is fixed to 1. Then expanding our line $\mathbf{w} = (w_0 , \dots, w_n)$, our evaluation function is still $\hat{f}(\mathbf{x}) = \left \langle \mathbf{w}, \mathbf{x} \right \rangle$. Excellent.

Now we can write our error function using the same style of compact notation. In this case, we will store all of our input data points $\mathbf{x}_j$ as rows of a matrix $X$ and the output values $y_j$ as entries of a vector $\mathbf{y}$. Forgetting the boldface notation and just understanding everything as a vector or matrix, we can write the deviation of the predictor (on all the data points) from the true values as

$y - Xw$

Indeed, each entry of the vector $Xw$ is a dot product of a row of $X$ (an input data point) with the coefficients of the line $w$. It’s just $\hat{f}$ applied to all the input data and stored as the entries of a vector. We still have the sign issue we did before, and so we can just take the square norm of the result and get the same effect as before:

$\displaystyle S(w) = \| y - Xw \|^2$

This is just taking a dot product of $y-Xw$ with itself. This form is awkward to differentiate because the variable $w$ is nested in the norm. Luckily, we can get the same result by viewing $y - Xw$ as a 1-by-$n$ matrix, transposing it, and multiplying by $y-Xw$.

$\displaystyle S(w) = (y - Xw)^{\textup{T}}(y-Xw) = y^{\textup{T}}y -2w^{\textup{T}}X^{\textup{T}}y + w^{\textup{T}}X^{\textup{T}}Xw$

This notation is widely used, in particular because we have nice formulas for calculus on such forms. And so we can compute a gradient of $S$ with respect to each of the $w_i$ variables in $w$ at the same time, and express the result as a vector. This is what taking a “partial derivative” with respect to a vector means: we just represent the system of partial derivates with respect to each entry as a vector. In this case, and using formula 61 from page 9 and formula 120 on page 13 of The Matrix Cookbook, we get

$\displaystyle \frac{\partial S}{\partial w} = -2X^{\textup{T}}y + 2X^{\textup{T}}Xw$

Indeed, it’s quite trivial to prove the latter formula, that for any vector $x$, the partial $\frac{\partial x^{\textup{T}}x}{\partial x} = 2x$. If the reader feels uncomfortable with this, we suggest taking the time to unpack the notation (which we admittedly just spent so long packing) and take a classical derivative entry-by-entry.

Solving the above quantity for $w$ gives $w = (X^{\textup{T}}X)^{-1}X^{\textup{T}}y$, assuming the inverse of $X^{\textup{T}}X$ exists. Again, we’ll spare the details proving that this is a minimum of the error function, but inspecting second derivatives provides this.

Now we can have a slightly more complicated program to compute the linear estimator for one input variable and many output variables. It’s “more complicated” in that much more mathematics is happening behind the code, but just admire the brevity!

from numpy import array, dot, transpose
from numpy.linalg import inv

def bestLinearEstimatorMV(points):
# input points are n+1 tuples of n inputs and 1 output
X = array([[1] + list(p[:-1]) for p in points]) # add bias as x_0
y = array([p[-1] for p in points])

Xt = transpose(X)
theInverse = inv(dot(Xt, X))
w = dot(dot(theInverse, Xt), y)
return w, lambda x: dot(w, x)


Here are some examples of its use. First we check consistency by verifying that it agrees with the test used in the two-variable case (note the reordering of the variables):

>>> print(bestLinearEstimatorMV(points)[0])
[ 6.97687136  0.50284939]

And a more complicated example:

>>> trueW = array([-3,1,2,3,4,5])
>>> bias, linearTerms = trueW[0], trueW[1:]
>>> points = [tuple(v) + (dot(linearTerms, v) + bias + noise(),) for v in [numpy.random.random(5) for _ in range(100)]]
>>> print(bestLinearEstimatorMV(points)[0])
[-3.02698484  1.03984389  2.01999929  3.0046756   4.01240348  4.99515123]

As a quick reminder, all of the code used in this post is available on this blog’s Github page.

## Bias and Variance

There is a deeper explanation of the linear model we’ve been studying. In particular, there is a general technique in statistics called maximum likelihood estimation. And, to be as concise as possible, the linear regression formulas we’ve derived above provide the maximum likelihood estimator for a line with symmetric “Gaussian noise.” Rather than go into maximum likelihood estimation in general, we’ll just describe what it means to be a “line with Gaussian noise,” and measure the linear model’s bias and variance with respect to such a model. We saw this very briefly in the test cases for the code in the past two sections. Just a quick warning: the proofs we present in this section will use the notation and propositions of basic probability theory we’ve discussed on this blog before.

So what we’ve done so far in this post is describe a computational process that accepts as input some points and produces as output a line. We have said nothing about the quality of the line, and indeed we cannot say anything about its quality without some assumptions on how the data was generated.  In usual statistical fashion, we will assume that the true data is being generated by an actual line, but with some added noise.

Specifically, let’s return to the case of two random variables $X,Y$. If we assume that $Y$ is perfectly determined by $X$ via some linear equation $Y = aX + b$, then as we already mentioned we can produce a perfect estimator using a mere two examples. On the other hand, what if every time we take a new $x$ example, its corresponding $y$ value is perturbed by some random coin flip (flipped at the time the example is produced)? Then the value of $y_i$ would be $y_i = ax_i + b + \eta_i$, and we say all the $\eta_i$ are drawn independently and uniformly at random from the set $\left \{ -1,1 \right \}$. In other words, with probability 1/2 we get -1, and otherwise 1, and none of the $\eta_i$ depend on each other. In fact, we just want to make the blanket assumption that the noise doesn’t depend on anything (not the data drawn, the method we’re using to solve the problem, what our favorite color is…). In the notation of random variables, we’d call $H$ the random variable producing the noise (in Greek $H$ is the capital letter for $\eta$), and write $Y = aX + b + H$.

More realistically, the noise isn’t chosen uniformly from $\pm 1$, but is rather chosen to be Gaussian with mean $0$ and some variance $\sigma$. We’d denote this by $\eta_i \sim N(\mu, \sigma)$, and say the $\eta_i$ are drawn independently from this normal distribution. If the reader is uncomfortable with Gaussian noise (it’s certainly a nontrivial problem to generate it computationally), just stick to the noise we defined in the previous paragraph. For the purpose of this post, any symmetric noise will result in the same analysis (and the code samples above use uniform noise over an interval anyway).

Moving back to the case of many variables, we assume our data points $y$ are given by $y = Xw + H$ where $X$ is the observed data and $H$ is Gaussian noise with mean zero and some (unknown) standard deviation $\sigma$. Then if we call $\hat{w}$ our predicted linear coefficients (randomly depending on which samples are drawn), then its expected value conditioned on the data is

$\displaystyle \textup{E}(\hat{w} | X) = \textup{E}((X^{\textup{T}}X)^{-1}X^{\textup{T}}y | X)$

Replacing $y$ by $Xw + H$,

$\displaystyle \begin{array} {lcl} \textup{E}(\hat{w} | X) & = & \textup{E}((X^{\textup{T}}X)^{-1}X^{\textup{T}}(Xw + H) | X) \\ & = & \textup{E}((X^{\textup{T}}X)^{-1}X^{\textup{T}}Xw + (X^{\textup{T}}X)^{-1}X^{\textup{T}}H | X) \end{array}$

Notice that the first term is a fat matrix ($X^{\textup{T}}X$) multiplied by its own inverse, so that cancels to 1. By linearity of expectation, we can split the resulting expression up as

$\textup{E}(w | X) + (X^{\textup{T}}X)^{-1}X^{\textup{T}}\textup{E}(H | X)$

but $w$ is constant (so its expected value is just itself) and $\textup{E}(H | X) = 0$ by assumption that the noise is symmetric. So then the expected value of $\hat{w}$ is just $w$. Because this is true for all choices of data $X$, the bias of our estimator is zero.

The question of variance is a bit trickier, because the variance of the entries of $\hat{w}$ actually do depend on which samples are drawn. Briefly, to compute the covariance matrix of the $w_i$ as variables depending on $X$, we apply the definition:

$\textup{Var}(\hat{w} | X) = \textup{E}(\| w - \textup{E}(w) \|^2 | X)$

And after some tedious expanding and rewriting and recalling that the covariance matrix of $H$ is just the diagonal matrix $\sigma^2 I_n$, we get that

$\textup{Var}(\hat{w} | X) = \sigma^2 (X^{\textup{T}}X)^{-1}$

This means that if we get unlucky and draw some sample which makes some entries of $(X^{\textup{T}}X)^{-1}$ big, then our estimator will vary a lot from the truth. This can happen for a variety of reasons, one of which is including irrelevant input variables in the computation. Unfortunately a deeper discussion of the statistical issues that arise when trying to make hypotheses in such situations. However, the concept of a bias-variance tradeoff is quite relevant. As we’ll see next time, a technique called ridge-regression sacrifices some bias in this standard linear regression model in order to dampen the variance. Moreover, a “kernel trick” allows us to make non-linear predictions, turning this simple model for linear estimation into a very powerful learning tool.

Until then!

# Eigenfaces, for Facial Recognition

This post assumes familiarity with the terminology and notation of linear algebra, particularly inner product spaces. Fortunately, we have both a beginner’s primer on linear algebra and a follow-up primer on inner products.

## The Quest

We are on a quest to write a program which recognizes images of faces. The general algorithm should be as follows.

1. Get a bunch of sample images of people we want to recognize.
2. Train our recognition algorithm on those samples.
3. Classify new images of people from the sample images.

We will eventually end up with a mathematical object called an eigenface. In short, an eigenface measures variability within a set of images, and we will use them to classify new faces in terms of the ones we’ve already seen. But before we get there, let us investigate the different mathematical representations of images.

## God has given you one face, and you make yourself a vector

Most naturally, we think of an image as a matrix of pixel values. For simplicity, we restrict our attention to grayscale images. Recall that a pixel value in the standard grayscale model is simply an unsigned byte representing pixel intensity. In other words, each pixel is an integer ranging from 0 to 255, where 0 is black and 255 is white. So every $n \times m$ image corresponds bijectively to a matrix with integer entries between 0 and 255.

Representing an image as a matrix reminds us of the ubiquitous applicability of linear algebra. Indeed, we may learn a great deal about our image by representing it with different bases or querying pixel neighbors. We can find frequencies, detect edges, and do a whole host of other fascinating things. However, we aren’t only concerned with the properties of one picture. In fact, individual pictures are useless to us! We only care about the relationships between a set of pictures, and we want to be able to compute “how similar” two faces are to each other.

In other words, we want a face space.

So instead of representing our images as a matrix, let’s represent them as points. Given an $n \times m$ matrix $A$ with entries $a_{i,j}$, we simply “collapse” the rows of a matrix into a single row like so:

$v = (a_{1,1}, \dots , a_{1,m}, a_{2,1}, \dots , a_{2,m}, \dots , a_{n,1}, \dots , a_{n,m})$

Now we have our face space: $\mathbb{R}^{n m}$. Note that even for small images, this space is huge. For example, with images of size $100 \times 100$, this space has ten-thousand dimensions! Certainly, reasoning about such objects would be impossible without a computer, but even then, we will run into trouble.

In order to translate back and forth between the standard matrix representation and the vector representation, we present the following Mathematica code. As always, the full notebook including all the code we provide here is available on this blog’s Github page.

imageToVector[img_] := Flatten[ImageData[img]];
vectorToImage[vec_, {n_, m_}] := Image[Partition[vec, m]];

Now all we need is a set of example faces. We found such an example from a facial recognition research group, and we posted the images on our Google code page. It contains a large number of images of size $200 \times 180$ pixels. Here are a few samples:

This bounty of data is excellent. Now we may begin to play.

## Mean Faces

In order to classify faces we would like to investigate the distribution of faces in face space. We start by computing the “mean face,” which represents the center of gravity of our sample of faces. We can do this simply by averaging the values of each pixel across all our face images.

We start by selecting a sample of male faces, at most one for each person in our database, and transforming them to grayscale:

(* This directory is particular to my file system. Change it
appropriately. *)

grayFaces = Map[ColorConvert[#, "Grayscale"] &, faces];

Then, we construct a vector with the average pixel intensity values for each pixel:

meanImage = Image[
Apply[Plus, Map[ImageData, grayFaces]] / Length[grayFaces]
]

ImageData accepts an image object (as represented in Mathematica) and returns the matrix of pixel values. Note that for this particular operation, which is just adding and dividing, it is unnecessary to translate faces from matrices to vectors.

The result is the following image:

Honestly, for a mean face I expected something more sinister. Just for kicks, let’s watch the averaging process incrementally:

This is a nice (seemingly fast) convergence. We casually wonder how much this particular image is subject to change with data coming from different cultures, as most of our data are twenty- or thirty-something white males.

Now that we have a mean, we may calculate the deviations of each image from our mean. Again, this is a simple subtraction of pixel values, which does not require special representation.

differenceFaces = Map[ImageSubtract[#, meanImage]&, grayFaces];

And get some pictures that look like these:

Don’t they look much nicer now that we’ve subtracted off the mean face? In all seriousness, we call these difference faces.

As one can see, some of these difference faces are darker than others. For whatever reason (perhaps some faces are in slightly more agreeable positions), the lighter faces are more “unique” in this sample of faces. We will see this notion come up again when we compute the actual eigenfaces: some will resemble the more variable faces.

While this is fine and dandy, we don’t yet have a way to recognize anybody. For this, we turn to statistics.

Covariance

We may interpret our face vectors in face space as a distribution of $n$ random variables which are precisely the people in the training sample. Specifically, when we try to classify a new face, we want to calculate the distance of that face from each of our training faces, and infer how likely it is to be a specific person.

In reality, however, since our faces are points in a space of $180 \times 200 = 36,000$ dimensions, we have $36,000$ random variables: one for each coordinate axis. While we recognize that this is far too many variables for us to do any computations with, we play along for now.

Taking our cue from statistics, we want to investigate the variability of the set of face vectors. To do this we use a special matrix called the covariance matrix of our distribution.

Definition: The covariance of two variables, denoted $\textup{Cov}(x,y)$, is the expected value of the product of their deviations from their means: $\textup{E}[(x-\textup{E}[x])(y-\textup{E}[y])]$.

Colloquially, the covariance measures the relationship between how two variables change. A high positive covariance means that a positive change in one variable correlates to a positive change in the other. A highly negative covariance implies a positive change in one variable correlates to a negative change in the other. If the two variables are independent, then their covariance is zero, though the reverse implication is not true in general.

As an example, consider the following distribution of points in the plane:

This distribution clearly has a nontrivial covariance in the $x$ and $y$ variables. It looks like an ellipse on a tilted axis signified by the two black arrows. In fact, we will soon be able to calculate those arrows; they have very special significance for us.

So just finding the variance of $x$ and $y$ (as in the coordinate axes) is not enough to fully describe this distribution. We need three numbers: the variance of $x$, the variance of $y$, and the covariance of $x,y$. As a side note, the variance of a variable is equivalently the covariance of that variable with itself.

We represent these three pieces of data in a covariance matrix, which for the two variables above has the form:

$\begin{pmatrix} \textup{Cov}(x,x) & \textup{Cov}(x,y) \\ \textup{Cov}(y,x) & \textup{Cov}(y,y) \end{pmatrix}$

Since the “Cov” as a function is symmetric in its arguments, we see that every covariance matrix is symmetric. This will have important implications for us, as symmetric matrices have a rich theory for us to exploit.

Once we compute the covariance matrix of our two variables, we want to describe the variance in terms of the “axes” as above. In other words, if we could just tilt, stretch, and move the data the right way, we could put it back on top of the normal unit coordinate axes $(1,0)$ and $(0,1)$. The process we just described is precisely a linear map that performs a change of basis. In particular, those two black arrows constitute the best basis to describe the data. So if we could just compute the right basis, we’d know everything about our distribution!

Now it’s no coincidence that the particular basis above is so nice. Specifically, if we consider our random variables as unit measurements along those axes (remember, one axis is stretched, so a unit measurement is longer there), then the two variables have zero covariance. In particular, the covariance matrix of the random variables in that basis is diagonal! Recalling that if a matrix has a diagonal representation, then the basis for that representation is a basis of eigenvectors. Hence, we see that computing any basis of eigenvectors will give us our optimal basis, should one exist.

And now, the coup de grâce, we recall that every symmetric real-valued matrix, and hence every covariance matrix, has an orthonormal basis of eigenvectors! This is a special case of the spectral theorem, which we discuss in our primer on inner product spaces.

So now we have proven that for any distribution of data in $n$ random variables, we may describe them with a basis of eigenvectors, such that the variables are pairwise uncorrelated. Let’s apply this to our face space.

## Methinks no eigenface so gracious is as mine

In one mindset, we may compute the covariance matrix of our difference faces quite easily:

differenceVectors = Map[imageToVector, differenceFaces];
covarianceMatrix = Transpose[differenceVectors].differenceVectors;

For the Java coders, note that the dot notation is for vector products; Mathematica is not object oriented, but rather it is functional with built in hash maps for mutation. Furthermore, in it’s raw form the “differenceVectors” variable is a 76-element list of face vectors.

These dot products, however, are precisely the computations of covariance, since each dot product computed is between two difference face vectors, each component of which is one of our random variables. Perform a few of the computations by hand (on a smaller matrix, obviously!) to convince yourself that it is so. The $i,j$ entry of our resulting covariance matrix is just $\textup{Cov}(x_i,x_j)$.

Here we are multiplying a $36,000 \times 76$ matrix (76 face vectors of length 36,000) by a $76 \times 36,000$ matrix, resulting an a $36,000 \times 36,000$ matrix. While we could theoretically compute the eigenvalues and eigenvectors of this matrix directly, in reality even the matrix multiplication to construct the matrix uses up all available memory and crashes the Mathematica kernel (how wimpy my netbook is that it can’t handle a billion-entry matrix!). In any case, we need another way to get our eigenvectors.

Again going back to linear algebra, we have a few useful propositions:

Proposition: If $A$ is a real matrix, then $AA^{\textup{T}}$ and $A^{\textup{T}}A$ are symmetric square matrices.

Proof. The squareness of these products is trivial. We prove symmetry of the first, and note the argument is identical for the second.

Here symmetry is equivalent to $A^{\textup{T}}A = (A^{\textup{T}}A)^{\textup{T}}$. We can see this immediately by using properties of transposition. Alternatively, we can note that the $i,j$ entry of the left side is the dot product of the $i$th row of $A^{\textup{T}}$ with the $j$th row of $A$. On the other hand, the $j,i$ entry is the dot product of the $j$th column of $A^{\textup{T}}$ and the $i$th column of $A$. Since the two factors are transposes of each other, the $i$th row of $A^{\textup{T}}$ is equal to the $i$th column of $A$, and similarly for the $j$s. In other words, the dot products described for the $i,j$ and $j,i$ entries are dot products of the same two vectors, and are hence equal. $\square$

In particular, we will use the symmetry about the smaller $AA^{\textup{T}}$ to get information about the eigenvectors of the larger $A^{\textup{T}}A$. We just need the following proposition:

Proposition: Let a real matrix $A$ have dimension $n \times m$, where $n \leq m$ (here a transposition of $A$ maintains generality). Then the number of eigenvectors with nonzero eigenvalue of $A^{\textup{T}}A \ (m \times m)$ is no greater than the number of eigenvectors with nonzero eigenvalue of $AA^{\textup{T}} \ (n \times n)$

In particular, the eigenvectors with zero eigenvalues are removable; they correspond to zero variability of a face in that particular direction. So for our purposes, it suffices to find the eigenvalues with nonzero eigenvalue. As we are about to see, this is generally much smaller than the total number of eigenvectors. In fact, the result above follows from a stronger statement:

Proposition: If $v$ is an eigenvector with nonzero eigenvalue of $A^{\textup{T}}A$, then $Av$ is an eigenvector with the same eigenvalue of $AA^{\textup{T}}$.

Proof. Letting $v$ be such an eigenvector, with corresponding eigenvalue $\lambda \neq 0$, we have $A^{\textup{T}}Av = \lambda v$, and hence

$(AA^{\textup{T}})Av = A(A^{\textup{T}}A)v = A \lambda v = \lambda Av$

This completes the proof. $\square$

By the same reasoning, if $v$ is an eigenvector with nonzero eigenvalue of $AA^{\textup{T}}$, then $A^{\textup{T}}v$ is an eigenvector of $A^{\textup{T}}A$.

Now we have just given a bijection between the eigenvectors with nonzero eigenvalue of $AA^{\textup{T}}$ and the much smaller $A^{\textup{T}}A$! This is great, because if we compute in our face problem, the former is a mere $76 \times 76$ matrix! Computing the eigenvectors is then a one-line affair, utilizing Mathematica’s fast library for it:

eigenfaceSystem = Eigensystem[covarianceMatrix];

This returns the eigenvalues in decreasing order, with their corresponding eigenvectors attached. Note that we still need to multiply them by the transpose of our “differenceVector” matrix.

The astute reader will notice that we named these eigenvectors eigenfaces. Though they are indeed just plain old eigenvectors, they have a special interpretation as images. Reforming them as $200 \times 180$ grayscale pictures, and scaling the values to $[0,255]$, we reveal truly haunting faces:

As creepy as they look, one must recall their astonishing interpretation: each ghostly face represents a random variable and the largest change of that random variable along its axis. Specifically, by finding these eigenfaces, we translated our notion of dimension from having one for each pixel to having one for each person in our training set, and these eigenfaces represent shared variability among the faces of those people. In other words, these faces represent the largest similarities between some faces, and the most drastic differences between others. This is one of the most stunning visualizations of dimension this mathematician has ever seen.

## This Face is Your Face, This Face is My Face

Though we only display a few above, we computed a basis of 76 different eigenfaces. Call the subspace spanned by these basis vectors (which is certainly a small subspace of $\mathbb{R}^{36,000}$) the eigenface subspace. For the purpose of learning new faces, we may reduce face space to the eigenface subspace, and hence represent any face as a linear combination of the eigenfaces.

To do this, we recall once again that the spectral theorem not only provided us a basis of eigenvectors, but it guaranteed that this basis is orthonormal. Recalling our primer, the projections of a vector onto elements of an orthonormal basis give us the needed coefficients for that vector’s expansion. If we label our eigenfaces $f_i$, then any face $c$ can be decomposed as

$c = \left \langle c, f_1 \right \rangle f_1 + \dots + \left \langle c,f_{76} \right \rangle f_{76}$

Here since we are working within $\mathbb{R}^{36,000}$, we may simply use the standard dot product as our inner product. Hence, in Mathematica the coefficients are easy to compute:

projectImageToFaceSpace[image_, meanImage_, eigenfaces_]:=
Module[{imageVec, diffVec, meanVec},
imageVec = imageToVector[image];
meanVec = imageToVector[meanImage];
diffVec = imageVec - meanVec;
Map[(diffVec.#)&, eigenfaces]
];

For the following face, we present its coefficients when projected onto each of the 76 eigenfaces, in order by decreasing eigenvalues.

coefficients =
{-6.85693, 23.7498, -11.4515, -3.43352, 5.24749, -7.1615,
8.09015, -9.7205, -0.660834, -2.4148, -10.3942, 3.33424,
2.94988, -2.75981, 3.02687, -2.4499, -2.09885, -5.98832,
-4.22564, -0.65014, 2.20144, -5.43782, -9.61821, -3.25227,
7.49413, -0.145002, 7.61483, -0.696994, -3.7731, 3.23569,
-1.78853, 0.0400116, -3.86804, -2.02456, 2.20949, -1.86902,
1.23445, 0.140996, 0.698304, -0.420466, 2.30691, 3.70434,
1.02417, 0.382809, 0.413049, -0.994902, 0.754145, 0.363418,
-0.383865, 1.46379, 1.96381, -2.90388, -2.33381, -0.438939,
-0.30523, -0.105925, 0.665962, -0.729409, -1.28977, 0.150497,
0.645343, 0.30724, -1.04942, 1.0462, -0.60808, 0.333288,
1.09659, -1.38876, 0.33875, 0.278604, 1.0632, -0.0446148,
0.24526, -0.283482, -0.236843, 0.312122};

And with the following piece of code, we can reconstruct the image exactly from the eigenfaces:

vectorToImage[
imageToVector[meanImage] + Apply[Plus, coefficients*eigenfaces],
{200, 180}]

While this is fine and dandy, we can do better computationally. As the magnitude of an eigenface’s corresponding eigenvalue decreases, we see, both visually and mathematically, that the eigenface contributes less to the overall variability of the distribution of faces. In other words, we can shed some of the shorter coordinate axes and still retain most of our description of face space!

Here is an animation of the face reconstruction where we choose the first $k$ eigenfaces (i.e., with the largest $k$ corresponding eigenvalues), and increase $k$:

We notice some “pauses” in the animation, which correspond to either very small coefficients or eigenfaces which only have small regions of variability.

Unfortunately, and this is in the author’s opinion an inherent wishy-washiness of applied mathematics, how many eigenfaces to use is an empirically determined variable. Exercising our best judgement, we find that the image reconstruction is easily recognizable after 30 eigenfaces are used (about seven seconds into the animation above), so we use those for our reduced eigenface subspace.

## Recognition Procedure

Now, to polish off our discussion, we have the quite simple recognition procedure. Once we have projected every sample image into the eigenface subspace, we have a bunch of points in $\mathbb{R}^{76}$.

When given a new image, all we need do is project it into face space, and find the smallest distance between the new point and each of the projections of the sample images. We do so in the Mathematica notebook (available from this blog’s Github page), and also run the recognition procedure on some pictures of the training subjects not used in training.

The reader is invited to look at the results, and see what sorts of changes in appearance mess up recognition the most. As a sneak preview, it seems that head-tilting, eye-closing, and smiling account for a lot of variability. If the reader is a fugitive on the run (and somehow find’s time to read this blog; I’m so honored!), and wishes to have his photograph not recognized, he should smile, tilt his head, and blink vigorously while the photograph is being taken. Additionally, to avoid detection, he should split some of his heist money with the author.

We notice one additional problem: in our haphazard way of selecting individuals for training and evaluation, we mistakenly attempted to classify some individuals that were never in a training photo. What’s worse, is that some of these men look strikingly similar to different people who were used in the training photos, with obvious differences like ears which stick out or thicker noses.

To handle subjects who were not in training, we need a category for those individuals who could not be classified. This is straightforward enough; we just have a minimum threshold. If an image is not within, say 25 units of any of our training faces, then it cannot be classified. This threshold can only be chosen empirically, which is again an unfortunate consequence of moving from pure mathematics to the real world.

With this modification, we see that our final evaluation sample of thirty faces, three of which were not in the original training set, results in only two false positive matches, and one false negative. This method appear to be relatively robust, considering its simplicity.

For more precise results, the reader is encouraged to play with the data and run larger tests. The faces used are organized by gender and name in a downloadable zip file on this blog’s Github page. The archive contains about twenty photos for each individual, and about eighty individuals. Have a blast!

## An Alternative Metric

While Euclidean distance is fine and dandy, there are a whole host of other methods for classifying points in Euclidean space. One could potentially train a neural network to perform classification, or dig deeper down the linear algebra rabbit hole and run the points through a support vector machine. Of course, with the rich ocean of literature on classification problems, there are likely many other applicable methods that the reader could explore. [Note: we intend to cover both neural networks and support vector machines on this blog in due time.]

However, there is one less drastic change we can make in our recognition procedure: ditch Euclidean distance. We may want to do so because our axes (the eigenfaces, which recall are eigenvectors of random variables), are at different scales. Euclidean distance hence treats one unit along a short axis as importantly as it treats a fraction of a longer axis. But the distances along shorter axes are more variable, and hence a small change there should mean more than it does elsewhere. This simply cannot do.

Our remedy is the Mahalanobis metric. It is specifically designed to utilize the covariance matrix to measure weighted distances.

Definition: Given a covariance matrix $S$, the Mahalanobis metric $m$ is defined as

$\displaystyle m(x,y) = \sqrt{(x-y)^{\textup{T}} S^{-1} (x-y) }$

However, since our covariance matrix is diagonal (using our basis of eigenvectors, the entries are just the corresponding eigenvalues), $S^{-1}$ is easy to compute: we just invert each diagonal entry. We encourage the reader to compare the two distance metrics’ performance in eigenface recognition. Augmenting our Mathematica notebook to do so should not require more than a few lines of code.

Finally, a few extensions and modifications of the eigenface method have popped up since their conception in the late 1980’s. In particular, they seek to compensate for the eigenface approach’s weakness to changes in lighting, orientation, and scaling. One such method is called “fisherfaces,” another “eigenfeatures,” and yet another the “active appearance model.” The latter two appear to map out “landmarks” on the image being processed, combining traditional facial metrics or anomalies with the eigenface decomposition technique. This author may return to an investigation of other facial recognition systems in the future, but for now we have too many other ideas.