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Restricted Deep Belief Networks for Multi-view Learning

  • Yoonseop Kang
  • Seungjin Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)

Abstract

Deep belief network (DBN) is a probabilistic generative model with multiple layers of hidden nodes and a layer of visible nodes, where parameterizations between layers obey harmonium or restricted Boltzmann machines (RBMs). In this paper we present restricted deep belief network (RDBN) for multi-view learning, where each layer of hidden nodes is composed of view-specific and shared hidden nodes, in order to learn individual and shared hidden spaces from multiple views of data. View-specific hidden nodes are connected to corresponding view-specific hidden nodes in the lower-layer or visible nodes involving a specific view, whereas shared hidden nodes follow inter-layer connections without restrictions as in standard DBNs. RDBN is trained using layer-wise contrastive divergence learning. Numerical experiments on synthetic and real-world datasets demonstrate the useful behavior of the RDBN, compared to the multi-wing harmonium (MWH) which is a two-layer undirected model.

Keywords

Hide Node Neural Information Processing System Mean Average Precision Restricted Boltzmann Machine Deep Belief Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yoonseop Kang
    • 1
  • Seungjin Choi
    • 1
    • 2
  1. 1.Department of Computer SciencePohang University of Science and TechnologyPohangKorea
  2. 2.Division of IT Convergence EngineeringPohang University of Science and TechnologyPohangKorea

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