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Unsupervised learning for MRFs on bipartite graphs
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
doi:10.5244/c.27.72
dblp:conf/bmvc/FlachS13
fatcat:iaheg3sclzbbfkrh46nahldneu