Multi-view clustering via robust consistent graph learning

Abstract

Multi-view clustering attracts considerable attention due to it could represent objects from different perspectives and thus provide complementary information for data analysis. Although previous multi-view clustering methods have achieved promising performance, there are still some limitations. For example, they decompose the original data features, and perform postprocessing to fulfill spectral clustering. Besides, they do not give sufficient consideration to the prevalent multi-view inconsistency in the initial graphs. In this article, we propose a novel robust consistent graph learning (RCGL) method to address these issues. More specifically, RCGL combines multi-view inconsistency and matrix factorization into the fusion graph learning, and learns the consensus similarity graph dynamically. By introducing the orthogonal and nonnegative constraint, the interpretable and robust clustering results can be obtained without any postprocessing steps. In addition, an alternative optimization strategy is presented to optimize the objective function. Experimental results on the real data sets demonstrate that the proposed method achieves the comparable or even better clustering performance than the state-of-the-art multi-view clustering methods.