Adaptive structure concept factorization for multiview clustering

Publication Type:
Journal Article
Neural Computation, 2018, 30 (4), pp. 1080 - 1103
Issue Date:
Filename Description Size
ContentServer.pdfPublished Version545.76 kB
Adobe PDF
Full metadata record
© 2018 Massachusetts Institute of Technology. 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-theart approaches in terms of accuracy, normalized mutual information, and purity.
Please use this identifier to cite or link to this item: