Robust Tensor Recovery for Incomplete Multi-View Clustering

Incomplete multi-view clustering is gaining increased attention owing to its great success in mining underlying information from the missing views. However, the existing approaches still encounter two issues: 1) They generally do not give sufficient consideration to the robustness of incomplete multi-view data with noise; 2) They only exploit the low-rank structures in the intra-view graphs, while the low-rank priors embedded in inter-view graphs are ignored. To this end, we propose a Robust Tensor Recovery for Incomplete Multi-view Clustering (RIMC) method, which transforms the view-missing problem into the tensor graph recovery problem by manipulating the comprehensive low-rank priors. Specifically, RIMC first employs a marginalized denoising operation to construct robust graphs and further builds a tensor graph by stacking these robust graphs. Then, we develop a novel tensor completion to recover the tensor graph by performing comprehensive low-rank priors: low-rank structures in the inter-view graphs (i.e., horizontal and lateral slices); low-rank structures in the intra-view graphs (i.e., frontal slices). Meanwhile, we integrate the tensor completion and spectral clustering to learn a unified indicator matrix. Extensive experiments show the promising performance of our method.