Multi-View Image Clustering Based on Sparse Coding and Manifold Consensus

Multi-view clustering has received an increasing attention in many applications, where different views of objects can provide complementary information to each other. Existing approaches on multi-view clustering mainly focus on extending Non-negative Matrix Factorization (NMF) by enforcing the constraint over the coefficient matrices from different views in order to preserve their consensus. In this paper, we argue that it is more reasonable to utilize the high-level manifold consensus rather than the low-level coefficient matrix consensus (as conducted in state-of-the-art approaches) to better capture the underlying clustering structure of the data. For this purpose, we propose MMRSC (Multiple Manifold Regularized Sparse Coding), which aims to preserve the consensus over multiple manifold structures from different views. Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task. Moreover, we also conduct computational complexity analysis and the result shows that MMRSC can effective handle the multi-view clustering problem without increasing the computational cost as compared to GraphSC.