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Adaptive structure concept factorization for multiview clustering

Adaptive structure concept factorization for multiview clustering

Zhan, Kun, Shi, Jinhui, Wang, Jing, Wang, Haibo and Xie, Yuange (2018) Adaptive structure concept factorization for multiview clustering. Neural Computation, 30 (4). pp. 1080-1103. ISSN 0899-7667 (Print), 1530-888X (Online) (doi:https://doi.org/10.1162/NECO_a_01055)

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: multi-view learning, concept factorization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 27 Apr 2021 15:17
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/30492

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