Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums
Machine Learning
2013-01-17 v1
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.
Cite
@article{arxiv.1301.3539,
title = {Learning Features with Structure-Adapting Multi-view Exponential Family Harmoniums},
author = {Yoonseop Kang and Seungjin Choi},
journal= {arXiv preprint arXiv:1301.3539},
year = {2013}
}
Comments
3 pages, 2 figures, ICLR2013 workshop track submission