Learning Features with Structure-Adapting Multi-view Exponential Family HarmoniumsDownload PDF

29 Mar 2024 (modified: 17 Jan 2013)ICLR 2013 conference submissionReaders: 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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