Incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning

Abstract

The incomplete multi-view clustering (IMVC) focuses on exploring hidden information from missing samples and complementary information from multiple views to enhance clustering performance. Most of existing methods for IMVC only attempt to obtain a shared consistent representation or graph from the non-missing samples based on matrix factorization. However, these methods rely too much on view alignment information, neglecting the specific-view structural information of different views and the hidden information of missing samples. To address the above problems, a novel method, called incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning (MSR_SIL), is proposed in this paper. Overall, MSR_SIL incorporates the missing-sample recovering in feature space and self-expressiveness coefficient learning in representation space into a unified framework. Specifically, a reconstruction term is introduced to recover missing samples with the help of non-missing ones so that incomplete multi-view is completed. Next, the self-expressiveness coefficient of the recovered complete multi-view is learned. Then, each self-expressiveness coefficient is decomposed into a consistent part to capture the similarity information of view-paired and a specific part to hold the unique information of each view. Especially, to retain the global manifold structure of the multi-view data, Schatten-p norm is applied. Finally, the clustering results can be obtained on the basis of the consistent and specific representation. Experimental results on eleven well-known benchmark multi-view datasets demonstrate that the proposed MSR_SIL yields significant improvements on clustering performance.