Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums

YoonSeop Kang, Seungjin Choi

Invalid Date (modified: Jan 17, 2013) ICLR 2013 conference submission readers: everyone
  • Decision: conferencePoster-iclr2013-workshop
  • Abstract: We proposea graphical model for multi-view feature extraction that automatically adapts its structure to achieve better representation of data distribution. The proposed model, structure-adapting multi-view harmonium (SA-MVH) has switch parameters that control the connection between hidden nodes and input views, and learn the switch parameter while training. Numerical experiments on synthetic and a real-world dataset demonstrate the useful behavior of the SA-MVH, compared to existing multi-view feature extraction methods.
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