Proceedings

Online Bayesian Max-Margin Subspace Multi-View Learning

Jia He, Changying Du, Fuzhen Zhuang, Xin Yin, Qing He, Guoping Long

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Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 1555-1560.

Abstract

Last decades have witnessed a number of studies devoted to multi-view learning algorithms, however, few efforts have been made to handle online multi-view learning scenarios. In this paper, we propose an online Bayesian multi-view learning algorithm to learn predictive subspace with max-margin principle. Specifically, we first define the latent margin loss for classification in the subspace, and then cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea. With the variational approximate posterior inferred from the past samples, we can naturally combine historical knowledge with new arrival data, in a Bayesian Passive-Aggressive style. Experiments on various classification tasks show that our model have superior performance.

Jia He, Changying Du, Fuzhen Zhuang, Xin Yin, Qing He, and Guoping Long. 2016. Online Bayesian Max-Margin Subspace Multi-View Learning. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16. IJCAI, 1555–1560. https://ijcai.org/proceedings/2016/223
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