Co-regularized weighting multiview clustering. Issue 3 (September 2017)

• Record Type: Journal Article
• Authors: You, Cong-Zhe; Wu, Xiao-Jun
• Is Part Of: Journal of algorithms & computational technology. Volume 11:Issue 3(2017)
• Page Start: 217
• Page End: 223
• Publication Date: 2017-09
• Subjects: Clustering -- co-regularization -- multiview; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301817701027
• Languages: English
• ISSNs: 1748-3018

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.