Multi-Graph-View Learning for Complicated Object Classification

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.

Authors

• Jia Wu
• Shirui Pan Griffith University, Queensland, Australia
• Xingquan Zhu Florida Atlantic University
• Zhihua Cai
• Chengqi Zhang Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University

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