Exploiting local structure in Boltzmann machines

Authors:
Hannes Schulz;Andreas Müller;Sven Behnke
Affiliations:
University of Bonn - Computer Science VI, Autonomous Intelligent Systems Group, Römerstraíe 164, 53117 Bonn, Germany;University of Bonn - Computer Science VI, Autonomous Intelligent Systems Group, Römerstraíe 164, 53117 Bonn, Germany;University of Bonn - Computer Science VI, Autonomous Intelligent Systems Group, Römerstraíe 164, 53117 Bonn, Germany
Venue:
Neurocomputing
Year:
2011

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Abstract

Restricted Boltzmann machines (RBM) are well-studied generative models. For image data, however, standard RBMs are suboptimal, since they do not exploit the local nature of image statistics. We modify RBMs to focus on local structure by restricting visible-hidden interactions. We model long-range dependencies using direct or indirect lateral interaction between hidden variables. While learning in our model is much faster, it retains generative and discriminative properties of RBMs of similar complexity.