Anchor-based incomplete multi-view spectral clustering

Abstract

In the past decade, multi-view clustering has become a research hot spot of machine learning. In traditional multi-view clustering methods, all views of the data points are assumed to be complete. However, in the practical applications, some views of data points may be missing and incomplete multi-view clustering methods are developed to handle these incomplete multi-view data. The existing incomplete multi-view clustering methods still have some defects such as insufficient use of missing information or neglecting the underlying relations among different views. To address these limitations, we propose an Anchor-based Incomplete Multi-view Spectral Clustering (AIMSC) approach. Specifically, AIMSC utilizes anchor points to connect all instances of each view and recover the missing information. Then, the similarities between all data points are derived from the similarities between data points and anchor points. Finally, anchor-based spectral clustering is executed to generate the clustering results. Experimental results on multiple benchmark datasets demonstrate the superiority of AIMSC.