首页|Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation
Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation
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NSTL
Elsevier
In the real world, one object is usually described via multiple views or modalities. Many existing multiview clustering methods fuse the information of multiple views by learning a consensus representation. However, the feature learned in this manner is usually redundant and has neglected the distinctions among the different views. Addressing this issue, a method named nonredundancy regularization based nonnegative matrix factorization with manifold learning (NRRNMF-ML) is proposed in the paper. A novel nonredundancy regularizer defined with the Hilbert-Schmidt Independence Criterion (HSIC) is incorporated in the objective function of the proposed method. By minimizing this term, the redundant information among the multiple views can be effectively reduced and the distinct contributions of the different views can be encouraged. To further utilizing manifold structure information of the data, a manifold regularizer is also constructed and included in the objective function of the proposed method. For the proposed method, an iterative optimization strategy was designed to solve the problem; the corresponding proof is presented both theoretically and experimentally in this paper. Experimental results on five multiview data sets compared with several representative multiview clustering methods revealed the effectiveness of the proposed method.
NSTL
Elsevier
