Journal
INFORMATION SCIENCES
Volume 536, Issue -, Pages 171-184Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.05.073
Keywords
Multi-view clustering; NMF; Orthogonal constraint; Normalization constraint
Categories
Funding
- Natural Science Foundation of China [61876044, 61672169]
- NSFCGuangdong Joint Found [U1501254]
- Guangdong Natural Science Foundation [2020A1515010670, 2020A1515011501]
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Multi-view clustering aims at integrating the complementary information between different views so as to obtain an accurate clustering result. In addition, the traditional clustering is a kind of unsupervised learning method, which does not take the label information into learning. In this paper, we propose a novel model, called semi-supervised multi-view clustering based on orthonormality-constrained nonnegative matrix factorization (MVOCNMF), to cluster the multi-view data into a number of categories. In the proposed model, based on the label information, we first learn the low-dimensional representations of data by the constrained NMF technique, and simultaneously cluster the samples with the same label into the clustering prototypes for each view. After that, we put forward a novel orthonormality constraint term to obtain the desirable representations for each view, and use the co-regularization to integrate the complementary information from different views. We further develop an alternating minimization algorithm to solve the proposed model, and present the convergence analysis and computational complexity of the proposed method. Extensive experimental results on several multi-view datasets have shown that the proposed MVOCNMF method outperforms the existing multi-view clustering methods. (C) 2020 Elsevier Inc. All rights reserved.
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