Journal
NEURAL COMPUTATION
Volume 30, Issue 4, Pages 1080-1103Publisher
MIT PRESS
DOI: 10.1162/neco_a_01055
Keywords
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Funding
- National Natural Science Foundation of China [61201422]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120211120013]
- Fundamental Research Funds for the Central Universities [lzujbky-2017-it73, lzujbky-2017-it76]
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Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views. With the interview correlation, a concept factorization-based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization-based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-the-art approaches in terms of accuracy, normalized mutual information, and purity.
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