Subspace-Contrastive Multi-View Clustering

Most multi-view clustering methods based on shallow models are limited in sound nonlinear information perception capability, or fail to effectively exploit complementary information hidden in different views. To tackle these issues, we propose a novel Subspace-Contrastive Multi-View Clustering (SCMC) approach. Specifically, SCMC utilizes a set of view-specific auto-encoders to map the original multi-view data into compact features capturing its nonlinear structures. Considering the large semantic gap of data from different modalities, we project multiple heterogeneous features into a joint semantic space, namely the embedded compact features are passed through the self-expression layers to learn the subspace representations, respectively. In order to enhance the discriminability and efficiently excavate the complementarity of various subspace representations, we use the contrastive strategy to maximize the similarity between positive pairs while differentiate negative pairs. Thus, the graph regularization is employed to encode the local geometric structure within varying subspaces for optimizing the consistent affinity matrix. Furthermore, to endow the proposed SCMC with the ability of handling the multi-view out-of-samples, we develop a consistent sparse representation (CSR) learning mechanism over the in-samples. To demonstrate the effectiveness of the proposed model, we conduct a large number of comparative experiments on ten challenging datasets, and the experimental results show that SCMC outperforms existing shallow and deep multi-view clustering methods. In addition, the experimental results on out-of-samples illustrate the effectiveness of the proposed CSR.