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Multi-View Classification via a Fast and Effective Multi-View nearest-subspace classifier

IEEE ACCESS 2019年 7卷 49669-49679页

作者： Shu, Ting Zhang, Bob Tang, Yuan Yan Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Multi-view data represented in multiple views contains more complementary information than a single view, whereby multi-view learning explores and utilizes the multi-view data. In general, most existing multi-view learning methods consider the correlation between multiple views. However, the relationship between classes and views which is also important in multi-view learning has never been involved in the existing works. In this paper, we propose a fast and effective multi-view nearest-subspace classifier (MV-NSC) by taking advantage of both the two relationships simultaneously. MV-NSC consists of four main parts: 1) projection residual, 2) view-dependent class separability, 3) view similarity, and 4) final decision. The last part combines the first three parts in one final decision matrix, while the first three parts utilize the information of the multi-view data in various aspects. Our proposed method is evaluated on four benchmark datasets and compared with seven other classifiers including both multi-and single-view algorithms. According to the experimental results, it shows that our proposed method is effective, efficient, and robust in multi-view classification.

关键词： Adaption model classification algorithm multi-view data nearest-subspace classifier view-dependent class separability view similarity

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