---
title: "Online Bayesian Max-Margin Subspace Multi-View Learning"
authors:
  - "Jia He"
  - "Changying Du"
  - "Fuzhen Zhuang"
  - "Xin Yin"
  - "Qing He"
  - "Guoping Long"
doi: ""
url: "https://ijcai.org/proceedings/2016/223"
pdf: "https://ijcai.org/proceedings/2016/0223.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1555-1560"
track: ""
keywords:
---

# Online Bayesian Max-Margin Subspace Multi-View Learning

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

*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.