Sample-level weights learning for multi-view clustering on spectral rotation

Abstract

Multi-view clustering usually yields better results than single-view clustering since it utilizes more information from multi-view data. However, in the original multi-view data, some samples may be corrupted in partial views. Under this situation, the locations of the corrupted data are often unknown. But there is limited literature regarding this problem. Moreover, most existing multi-view spectral clustering methods need a post-processed algorithm k-means after obtaining the partition matrix, which leads to deviations in the clustering results. To resolve these problems, we propose a multi-view spectral clustering method named sample-level weights learning for Multi-view Clustering on Spectral Rotation (SR-MC) in this paper. By learning the weights in the sample level, SR-MC can make full use of the helpful complementary information among different views while reducing the effects of low-quality data for each sample. Therefore, it can deal with various multi-view clustering scenarios such as data are complete or corrupted in partial views. To reduce the deviations of the clustering results, a joint framework is designed for combining the learning of the consensus Laplacian matrix, the real-valued partition matrix and the binary indicator matrix together. The objective function of SR-MC can be efficiently optimized by an alternative optimization algorithm. Compared with other ten baselines, experiments on seven datasets show the superiority of our method.