Co-regularized weighting multiview clustering

Abstract

This paper deals with clustering for multiview data. Multiview clustering has been a research hot spot in many domains or applications, such as information retrieval, biology, chemistry, and marketing. Exploring information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. The aim is to search for clustering patterns that perform a consensus between the patterns from different views. Inspired by variable weighting and co-regularized strategy, this paper studies co-regularized weighting multiview clustering algorithms. Two co-regularized weighting multiview clustering algorithms are proposed from two aspects: pairwise co-regularization and centroid-based co-regularization. Experimental results obtained both on synthetic and real datasets show that the proposed algorithms outperform the main existing multiview clustering algorithms.