Multiview Clustering Based on Non-Negative Matrix Factorization and Pairwise Measurements
Wang Xiu, Tianzhen Zhang, Xinbo Gao · IEEE Transactions on Cybernetics · 2018
As we all know, multiview clustering has become a hot topic in machine learning and pattern recognition. Non-negative matrix factorization (NMF) has been one popular tool in multiview clustering due to its competitiveness and interpretation. However, the existing multiview clustering methods based on NMF only consider the similarity of intra-view, while neglecting the similarity of inter-view. In this paper, we propose a novel multiview clustering algorithm, named multiview clustering based on NMF and pairwise measurements, which incorporates pairwise co-regularization and manifold regularization with NMF. In the proposed algorithm, we consider the similarity of the inter-view via pairwise co-regularization to obtain the more compact representation of multiview data space. We can also obtain the part-based representation by NMF and preserve the locally geometrical structure of the data space by utilizing the manifold regularization. Furthermore, we give the theoretical proof that the objective function of the proposed algorithm is convergent for multiview clustering. Experimental results show that the proposed algorithm outperforms the state-of-the-arts for multiview clustering.