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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:CPCI-S

Volume:2017-November

Page Number:6-10

Key Words:multi-view clustering; self-paced learning; Self-Paced regularizer; spectral clustering

Abstract:Multi-view data are prevalent in both machine learning and artificial intelligence. A panoply of multi-view clustering algorithms have been proposed to deal with multiview data. However, most of them just blindly concatenate all the views in spite of characteristic of different views. Self-paced learning is a kind of learning scheme which comes from human learning. It progresses from easy example to complex example during learning process. In analogy with these intuitions, we can learn the easiness of multiple views. Therefore, in this paper, we first present a new Self-Paced Learning Regularizer which is a kind of mixture weighting scheme to allocate different weight to the different view by considering views' complexity. To recap the effectiveness of our self-paced learning regularizer, we propose a novel self-paced learning based multi-view spectral clustering algorithm(SPLMVC), which can define complexity across views and then automatically assign weight to each view. Extensive experiments on real-world multi-view datasets reveal its strength by comparison with other state-of-art methods.