{"type":"ScholarlyArticle","title":"Online Bayesian Max-Margin Subspace Multi-View Learning","authors":[{"name":"Jia He","slug":"jia-he","given":"Jia","family":"He","affiliation":""},{"name":"Changying Du","slug":"changying-du","given":"Changying","family":"Du","affiliation":""},{"name":"Fuzhen Zhuang","slug":"fuzhen-zhuang","given":"Fuzhen","family":"Zhuang","affiliation":""},{"name":"Xin Yin","slug":"xin-yin","given":"Xin","family":"Yin","affiliation":""},{"name":"Qing He","slug":"qing-he","given":"Qing","family":"He","affiliation":""},{"name":"Guoping Long","slug":"guoping-long","given":"Guoping","family":"Long","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2016/223","pdf":"https://ijcai.org/proceedings/2016/0223.pdf","markdown":"https://ijcai.org/proceedings/2016/223.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/223","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":223,"pages":{"first":1555,"last":1560},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}