Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization.

Wang, J., Wang, X., Tian, F., Liu, C. H., Yu, H. and Liu, Y., 2016. Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization. In: 23rd International Conference on Neural Information Processing, 16--21 October 2016, Kyoto, Japan, pp. 435-444.

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Official URL: http://link.springer.com/chapter/10.1007%2F978-3-3...

DOI: 10.1007/978-3-319-46672-9_49

Abstract

Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using L2,1-norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.

Item Type:Conference or Workshop Item (Paper)
ISSN:0302-9743
Series Name:Lecture Notes in Computer Science
Uncontrolled Keywords:Nonnegative Matrix Factorization; Multi-view learning; Semi- supervised learning
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:24965
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:17 Nov 2016 11:33
Last Modified:17 Nov 2016 11:35

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