Multiview clustering based on Robust and Regularized Matrix Approximation

Pattern recognition tasks such as the data classification and clustering usually can be represented by the perspective of multiple views or feature spaces. Obviously, the accuracy of the classification and clustering should be greatly improved if we carefully consider the discriminabilities from multiple views and explore the complementary information among them. However, multiple features also bring new challenges to handle them. In the literature, many existed multiview feature learning methods dealt with different views equally, thus they couldn't optimally utilize the complementary property of them. On the other hand, the matrix factorization based clustering algorithms usually adopt the conventional ℓ2-norm based squared residue minimization to measure the loss, which is easily influenced by the outliers and noises from the multiple sources of input. In this paper, we propose a novel multiview data clustering algorithm based on the matrix factorization to relieve the above issues. The basic idea of the proposed Robust and Regularized Matrix Approximation (RRMA) is that the observed data matrix could be low-rank approximated by a cluster centroid matrix and a cluster indicator matrix, respectively, and the major contributions of our work lie in the introduction of the robust ℓ2,1-norm and ensemble manifold regularization to regularize the matrix factorization and make the model more discriminative for multiview data clustering. We properly adjust the importance of different views by assigning a set of trainable weights on the views. Moreover, we propose an efficient solution featured with impactful updating rules to seek the local optimal parameters. Encouraging experimental results on numerous public multiview datasets demonstrate the superiority of our model compared to some state-of-the-art methods.