Posterior Regularization for Expectation Maximization

Summary

The Expectation Maximization algorithm is a method for finding the maximum likelihood estimates for the parameters in a statistical model. During the E-step of this algorithm, posterior probabilities are calculated for the latent data by fixing the parameters.

In many fields, prior knowledge about the posterior probabilities are known and can be applied to the model to improve the statistical model, yet the method to include such knowledge is not always clear.

Posterior Regularization is a method used to impose contraints on posteriors in the Expectation Maximization algorithm, allowing a finer-level control over these posteriors, which allows prior knowledge to be defined.

Method Description

For a given set $x\in X$ of observed data, a set of latent data $z\in Z$ and a set of parameters $\theta$, the Expectation Maximization algorithm can be viewed as the alternation between two maximization steps of the function $F(q,\theta )$, by marginalizing different free variables.

The E-step is defined as:

$q^{t+1}=argmax_{q}F(q,\theta ^{t})=argmax_{q}[-D_{KL}(q||p_{\theta ^{t}}(z|x))]=p_{\theta ^{t}}(z|x)$

where $D_{KL}$ is the Kullback-Leibler divergence given by $D_{KL}(q||p)=E_{q}[log{\frac {q}{p}}]$, and q(z|x) is an arbitrary probability distribution over the latent variable z.

The M-step is defined as:

$\theta ^{t+1}=argmax_{\theta }F(q^{t+1},\theta )=argmax_{\theta }\ E_{X}[\sum _{s}q^{t+1}(z|x)\ log\ p_{\theta }(x,z)]$

The goal of this method is to define a way to constrains over posteriors.