Self-weighting multi-view spectral clustering based on nuclear norm

Multi-view clustering attracts more and more attention due to the fact that it can utilize the complementary and compatible information from multi-view data sets. In many graph-based multi-view clustering approaches, the graph quality is important since it influences the following clustering performance. Therefore, learning a high quality similarity graph is desired. In this paper, we propose a novel clustering method which is named as Self-weighting Multi-view Spectral Clustering based on Nuclear Norm (SMSC_NN). Specifically, to fully utilize the multiple view features, the common consensus representation is learned. Moreover, to capture the principal components from various view features, the nuclear norm is introduced which can make the view-specific information be well explored. Further, due to the fact that each view feature denotes a sort of specific property, the adaptive weights are assigned instead of equal view weights. In order to verify the effectiveness of the proposed method, four multi-view data sets are used to conduct the clustering experiments. Extensive experimental results demonstrate the superiority of the proposed method comparing with state-of-the-art multi-view clustering approaches. In addition, the proposed approach is experimented on the Cal101-20 data set with ”salt and pepper” noises, and experimental results verify that the proposed SMSC_NN method can remain robust to noises.