Abstract

Multi-view clustering has become a hot yet challenging topic, due mainly to the independence of and information complementarity between different views. Although good results are achieved to a certain extent from typical methods including multi-view based -means clustering, sparse cooperative representation clustering and subspace clustering, they still suffer from several drawbacks or limitations: (1) When each view is sparse decomposed, it still contains some hidden information for mining, such as the structure of samples, the intra-class similarity measure, and the inter-class diversity discrimination, etc. (2) Most of the existing multi-view methods only consider the local features within each view, but fail to effectively balance the importance of and combine information among different views in a diversified way. To tackle these issues, we propose a novel multi-view diversity learning model based on robust bilinear error decomposition (BED). The BED term with a low rank sparse constraint is an improved non-negative matrix factorization (NMF), which is used to extract the hidden structure information in sparse decomposition and useful diversity discrimination information in error matrix. The preservation of local features and selection of important views are achieved by adaptive weighted manifold learning. Furthermore, the Hilbert Schmidt independence criterion is used as a diversity learning term for mutual learning and fusion among views. Finally, the proposed robust low-rank multi-view diversity learning spectral clustering method is evaluated and benchmarked with eight state-of-the-art methods. Experiments in six real datasets have fully validated the significantly improved accuracy and efficiency of the proposed methodology for effective clustering of multi-view images.