Unsupervised learning for MRFs on bipartite graphs

Boris Flach, Tomas Sixta
2013 Procedings of the British Machine Vision Conference 2013  
We consider unsupervised (parameter) learning for general Markov random fields on bipartite graphs. This model class includes Restricted Boltzmann Machines. We show that besides the widely used stochastic gradient approximation (a.k.a. Persistent Contrastive Divergence) there is an alternative learning approach -a modified EM algorithm which is tractable because of the bipartiteness of the model graph. We compare the resulting double loop algorithm and the PCD learning experimentally and show
more » ... at the former converges faster and more stable than the latter.
doi:10.5244/c.27.72 dblp:conf/bmvc/FlachS13 fatcat:iaheg3sclzbbfkrh46nahldneu