Multi-graph-view learning for complicated object classification

Jia Wu, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

In this paper, we propose to represent and classify complicated objects. In order to represent the objects, we propose a multi-graph-view model which uses graphs constructed from multiple graph-views to represent an object. In addition, a bag based multi-graph model is further used to relax labeling by only requiring one label for a bag of graphs, which represent one object. In order to learn classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), which aims to explore subgraph features from multiple graphviews for learning. By enabling a joint regularization across multiple graph-views, and enforcing labeling constraints at the bag and graph levels, MGVBL is able to discover most effective subgraph features across all graph-views for learning. Experiments on real-world learning tasks demonstrate the performance of MGVBL for complicated object classification.

Original languageEnglish
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsQiang Yang, Michael Wooldridge
PublisherInternational Joint Conferences on Artificial Intelligence
Pages3953-3959
Number of pages7
Volume2015-January
ISBN (Electronic)9781577357384
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Conference

Conference24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period25/07/1531/07/15

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  • Cite this

    Wu, J., Pan, S., Zhu, X., Cai, Z., & Zhang, C. (2015). Multi-graph-view learning for complicated object classification. In Q. Yang, & M. Wooldridge (Eds.), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence (Vol. 2015-January, pp. 3953-3959). International Joint Conferences on Artificial Intelligence.