Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums
This work addresses feature extraction in multi-view data, but it appears incremental as it builds on existing harmonium models with added structure adaptation.
The paper tackled multi-view feature extraction by proposing a graphical model that adapts its structure to better represent data distribution, demonstrating useful behavior compared to existing methods in experiments on synthetic and real-world datasets.
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.