关键词: 非负矩阵分解, 多视图学习, 数据聚类, 流形正则化

Abstract: To address the multi-view data clustering for the multi-media data with various of feature representations as input, this paper proposes a Multi-view Regularized Matrix Factorization（MRMF） algorithm based on the well-known Nonnegative Matrix Factorization（NMF）. In detail, the proposed MRMF extends the conventional NMF to its multi-view version which considers multiple matrices as input, and then introduces the [L2,1] norm to measure the loss of matrix approximation. Furthermore, the multi-view manifold regularization is also considered to regularize the proposed matrix factori-
zation. Compared with some existing data clustering as well as multi-view clustering algorithms, the proposed MRMF is less sensitive to noises in the original data and can also better balance the importance of each view by maintaining a set of learnable weights for each view in the manifold regularization. Encouraging experimental results on numerous public multi-view datasets demonstrate the superiority of the model compared to some state-of-the-art methods.

Key words: nonnegative matrix factorization, multi-view learning, data clustering, manifold regularization