On Contrastive Divergence Learning
Maximum-likelihood (ML) learning of Markov random fields is challenging because it requires estimates of averages that have an exponential number of terms. Markov chain Monte Carlo methods typically take a long time to converge on unbiased estimates, but Hinton (2002) showed that if the Markov chain is only run for a few steps, the learning can still work well and it approximately minimizes a different function called “contrastive divergence” (CD). CD learning has been successfully applied to various types of random fields. Here, we study the properties of CD learning and show that it provides biased estimates in general, but that the bias is typically very small. Fast CD learning can therefore be used to get close to an ML solution and slow ML learning can then be used to fine-tune the CD solution. Consider a probability distribution over a vector x (assumed discrete w.l.o.g.) and with parameters W p(x;W) = 1 Z(W) e (1) where Z(W) = ∑ x e −E(x;W) is a normalisation constant and E(x;W) is an energy function. This class of random-field distributions has found many practical applications (Li, 2001; Winkler, 2002; Teh et al., 2003; He et al., 2004). Maximum-likelihood (ML) learning of the parameters W given an iid sample X = {xn}n=1 can be done by gradient ascent: W = W + η ∂L(W;X ) ∂W ∣