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 PaperResearchpeer-review

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 publicationProceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence
EditorsQiang Yang, Michael Wooldridge
Place of PublicationPalo Alto CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages3953-3959
Number of pages7
ISBN (Electronic)9781577357384
Publication statusPublished - 2015
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 2015 - Buenos Aires, Argentina
Duration: 25 Jul 20151 Aug 2015
Conference number: 24th
https://www.ijcai-15.org/index.php?option=com_content&view=article&id=71:call-for-papers&catid=9:uncategorised&Itemid=477

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 2015
Abbreviated titleIJCAI 2015
CountryArgentina
CityBuenos Aires
Period25/07/151/08/15
Internet address

Cite this