Robust multi-view clustering via structure regularization concept factorization

Recently, many concept factorization-based multi-view clustering methods have been proposed and achieved promising results on text multi-view data. However, existing methods are limited in the following two aspects. (1) The Frobenius norm used in these methods is sensitive to noise and outliers; (2) These methods ignore the global structural information of the data. To address the above problems, we propose a robust concept factorization framework for multi-view clustering, which not only improves the robustness but also fully exploits the available information of multi-view data. Specifically, L 2,1-norm is used to evaluate the error of the factorization, thus eliminating the effect of outliers and improving robustness. In addition, to retain more structure information of the original data, the global and local structure information are taken into consideration simultaneously, which makes the learned low-dimensional matrix more discriminative. Further, to make use of the complementary information of the different views, we introduce an adaptive weight learning strategy to assign weights for different views. An iterative updating algorithm is proposed to solve the proposed optimization problem. We compare the proposed method with state-of-the-art alternative methods on benchmark multi-view data sets. The extensive experimental results show the effectiveness and superiority of the proposed method.