This first function generates a dataset from a mixture of Gaussians:

# generating n datapoints from a mixture of K Gaussians with dimensions d
# k  : the respective datapoint classes
# mu : kxd matrix with means
# sig: kxdxd matrix with dxd covariate matrices
gen.mix <- function(n, k, mu, sig) {

  d <- length(mu[1,])  # number of dimensions
  result <- matrix(rep(NA,n*d), ncol=d)
  colnames(result) <- paste0("X",1:d)
  for(i in 1:n) {
    result[i,] <- mvrnorm(1, mu = mu[k[i],], Sigma=sig[,,k[i]])

So, let’s make one dataset from a mixture of three gaussians to try our stuff:

n <- 360

mu <- matrix(c(4.0,4.0,
               6.5,  5), ncol=2, byrow=T)

sigs <- array(rep(NA,2*2*3), c(2,2,3))  # 3D matrix
sigs[,,1] <- matrix(c(.25, .21, .21,.25), nrow=2, byrow=TRUE)
sigs[,,2] <- matrix(c(.25,-.21,-.21,.25), nrow=2, byrow=TRUE)
sigs[,,3] <- matrix(c(.25, .21, .21,.25), nrow=2, byrow=TRUE)

pi <- c(.2,.5,.3) # mixing coeffs
classes <- sample(1:3, n, replace=TRUE, prob=pi)

mydata <- gen.mix(n, classes, mu, sigs)

We can plot the complete dataset, i.e., including the distribution that produced each datapoint:

plot(mydata, col=c("red","green","blue")[classes], xlab="X1", ylab="X2", pch=19)

But usually we do not know the details of the mixing process, so we only have the incomplete dataset:

plot(mydata, col="black", xlab="X1", ylab="X2", pch=19)

The Model

If we decide to model the previous dataset as a mixture of \(K\) Gaussians, then for a given datapoint \(x\):

\[p(x) = \sum_{i=1}^K p(K=i)p(x|K=i) = \sum_{i=1}^K \pi_k \mathcal{N}(x|\mu_k,\Sigma_k)\]

is a superposition of \(K\) Gaussians. Each density \(\mathcal{N}(x|\mu_k,\Sigma_k)\) is a component of the mixture with its own mean and covariance matrix. The parameters \(\pi_k\) are called the mixing coefficients, such that \(\sum_k \pi_k = 1, \pi_k \geq=0\) (\(\pi_k\) are probabilities).

An important value is \(\gamma_k(x) \equiv p(K=k|x)\) which is called the responsability of Gaussian \(k\) over datapoint \(x\). By Bayes theorem,

\[\gamma_k(x) \equiv p(K=k|x) = \frac{p(K=k)p(x|K=k)}{\sum_i p(K=i)p(x|K=i)} = \frac{\pi_k \mathcal{N}(x|\mu_k,\Sigma_k)}{\sum_i \pi_i \mathcal{N}(x|\mu_i,\Sigma_i)}\]

The parameters of this model are \(\pi \equiv \{\pi_1,\ldots,\pi_K\}\), \(\mu \equiv \{ \mu_1,\ldots,\mu_K \}\) and \(\Sigma \equiv \{\Sigma_1,\ldots,\Sigma_k\}\).

The log-likelihood of the dataset \(X \equiv \{X_1,\ldots,X_N\}\) given the parameters is

\[\log p(X|\pi,\mu\Sigma) = \sum_{n=1}^N \log \left\{ \sum_{k=1}^K \pi_k \mathcal{N}(x|\mu_k,\Sigma_k) \right\}\]

Notice that there is not a closed-form analytic solution for the MLE. This is a good eg where the Expectation-maximization (EM) algorithm can find a numerical solution.

The EM solution

We have an incomplete dataset \(X\) with no information about which density produced each datapoint. Let’s call the parameters \(\theta = \{\pi,\mu,\Sigma\}\).

So, we introduce a K-dimensional binary random variable \(Z\), where each concretization \(z\) consists of a vector of zeros except for one coordinate that has a one (eg for \(K=5\), \(z=(0,0,0,1,0)\)). So, there are \(K\) possible different states of \(z\). Let’s call \(z_k\) the k-th coordinate of \(z\).

The marginal distribution is specified in terms of the mixing coefficient \[p(z_k=1) = \pi_k \iff p(z) = \prod_{k=1}^K \pi_k^{z_k}\] since \(z\) has zero everywhere else.

The conditional distribution of \(x\) given a value \(z\) is \[p(x|z_k=1) = \mathcal{N}(x|\mu_k,\Sigma_k)\] which can be also stated as \[p(x|z) = \prod_{k=1}^K \mathcal{N}(x|\mu_k,\Sigma_k)^{z_k}\]

With these two distributions we can compute the joint distribution \(p(x,z)\)

\[p(x,z) = p(x|z)p(z) = \prod_{k=1}^K ( \pi_k \mathcal{N}(x|\mu_k,\Sigma_k) )^{z_k}\]

The marginal distribution of \(x\) becomes

\[p(x) = \sum_z p(z)p(x|z) = \sum_{k=1}^k \pi_k \mathcal{N}(x|\mu_k,\Sigma_k)\]

which corresponds to the original mixing Gaussian model from the previous section. This means that this new model with latent variables \(z\) is equivalent to the original model.

The advantage is that the original model could not be used with MLE, but this new model can be used in the EM algorithm!

The likelihood of the complete dataset \(\{X,Z\}\) is

\[p(X,Z|\theta) = \prod_{n=1}^N \prod_{k=1}^K ( \pi_k^{z_k} \mathcal{N}(x|\mu_k,\Sigma_k) )^{z_k}\]

so the log-likelihood:

\[\log p(X,Z|\theta) = \sum_{n=1}^N \sum_{k=1}^K z_k \left\{ \log \pi_k + \log \mathcal{N}(x|\mu_k,\Sigma_k) \right\}\] check and compare with the previous log-likelihood of incomplete dataset \(X\); this one is much simpler for a MLE solution.

Using the formulas for marginal \(p(z)\) and the conditional \(p(x|z)\), and Bayes theorem, we can find the posterior distribution

\[p(Z|X,\theta) \propto \prod_{n=1}^N \prod_{k=1}^K ( \pi_k^{z_k} \mathcal{N}(x|\mu_k,\Sigma_k) )^{z_k}\]

The e-step of the EM is finding the value

\[E_{Z|X,\theta} [ \log p(X,Z|\theta) ] = \sum_{n=1}^N \sum_{k=1}^K \gamma_k(x_i) \left\{ \log \pi_k + \log \mathcal{N}(x|\mu_k,\Sigma_k) \right\}\]

For that we need to compute each responsability \(\gamma_k(x_i)\) using the current parameter values \(\theta\). To recall the expression:

\[\gamma_k(x) \equiv p(K=k|x) = \frac{p(K=k)p(x|K=k)}{\sum_i p(K=i)p(x|K=i)} = \frac{\pi_k \mathcal{N}(x|\mu_k,\Sigma_k)}{\sum_i \pi_i \mathcal{N}(x|\mu_i,\Sigma_i)}\]

The m-step fixes the responsabilities and maximizes the previous expected value wrt \(\theta\). These updates have closed-forms (we do not to compute the expected value):

After an iteration (e-step plus m-step) we check if the parameters are within convergence tolerance, and if not, run another iteration.

Fitting mixtures with R

The package mixtools provides a set of functions for analyzing a variety of finite mixture models, and some functions use EM methods.

Herein, we use mvnormalmixEM which runs the EM algorithm for mixtures of multivariate normal distributions:

## Loading required package: boot
## Loading required package: segmented
## mixtools package, version 1.0.3, Released 2015-04-18
## This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
model <- mvnormalmixEM(mydata, k=3, epsilon=1e-04)
## number of iterations= 28
## [[1]]
## [1] 4.949747 5.067823
## [[2]]
## [1] 3.946154 3.906414
## [[3]]
## [1] 6.499421 4.945130
## [[1]]
##            [,1]       [,2]
## [1,]  0.1881327 -0.1490038
## [2,] -0.1490038  0.1865688
## [[2]]
##           [,1]      [,2]
## [1,] 0.2568662 0.2071650
## [2,] 0.2071650 0.2108472
## [[3]]
##           [,1]      [,2]
## [1,] 0.2336036 0.2014786
## [2,] 0.2014786 0.2310154
plot(model, which=2)

##            comp.1       comp.2       comp.3
## [1,] 9.999992e-01 8.452227e-07 1.071409e-21
## [2,] 9.708542e-01 2.914575e-02 3.559954e-11
## [3,] 1.264604e-12 2.189724e-14 1.000000e+00
## [4,] 3.337290e-06 1.759710e-11 9.999967e-01
## [5,] 6.714612e-01 3.285387e-01 9.815883e-08
## [6,] 9.829551e-01 1.704491e-02 3.338266e-12
pred <- apply(model$posterior, 1, function(row) which.max(row))
table(classes, pred)
##        pred
## classes   1   2   3
##       1   5  69   0
##       2 173   0  12
##       3   1   0 100

There is an error rate of 5% (the different number labels are meaningless). This is a consequence that the point clouds of different classes mix with each other, which simply cannot be recovered with this model (and, arguably, not by any other).