Graph embedded incomplete multi‐view clustering method with proximity relation estimation

Abstract

More and more importance has been attached to multi-view clustering due to its outstanding performance against single-view clustering. Existing studies are on the basis of the assumption that every sample exists in all views. However, in reality, this is often not the case and the real data are always with incomplete views, which lead to the failure of the conventional methods. To address the issue, the authors present a novel method, referred to as incomplete multi-view spectral clustering with proximity relation estimation (IMSC_PRE), which exploits the proximity relations of samples in different views to complete their affinity graphs. Different from existing methods, IMSC_PRE fully excavates the manifold structures of all views to estimate the proximity relations of missing samples in each view, which presents a brand-new strategy to exploit the proximity information lying in the whole samples with good interpretation. Experimental results on four datasets demonstrate the advantages of the proposed method in comparison with the state-of-the-art methods.