School of Software，Dalian University of Technology，Dalian，116620，China
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Published
2017-05-30
Issue Date
2017-05-30
Abstract
In the era of big data，data often comes from multiple feature extractors or consists of multiple views.Multiview clustering is one of the common approaches for the analysis of such data，which separates data into several groups using information from multiple views.Multiview clustering via multimanifold regularized nonnegative matrix factorization has become one of the most modern multiview clustering algorithms in the past decade.However，they do not consider the cluster permutation in nonnegative matrix factorization，and they equally treat each view in the experiment.Based on the above issues，in this paper，an improved multimanifold regularized multiview nonnegative matrix factorization algorithm has been proposed.The key problems of this algorithm are the way of clustering multiview data and the integration of multimanifold.In the process of clustering multiview data，the multiview data share the same low dimensional submatrix and the weighted objective function of all views is minimized，which indicates the importance of each view in clustering and ensures the consistency of cluster permutations in nonnegative matrix factorization.In the process of integrating multiple manifolds，the consensus manifold is approximated by the weighted linear combination of multiple manifolds.To show the importance of each view’s manifold，the weighting schema based on multiview spectral clustering is adopted to find the consensus manifold.To show the effectiveness of the proposed strategy，we experiment on several benchmark datasets.Experimental results show that the proposed algorithm outperforms the state of the art and the proposed optimization strategies are effective for multiview clustering.
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