Learning Features with Structure-Adapting Multi-view Exponential Family
    Harmoniums
Authors: YoonSeop Kang, Seungjin Choi

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.

Decision: conferencePoster-iclr2013-workshop