Spectral clustering with distinction and consensus learning on multiple views data

Peng Zhou, Fan Ye, Liang Du
2018 PLoS ONE  
Since multi-view data are available in many real-world clustering problems, multi-view clustering has received considerable attention in recent years. Most existing multi-view clustering methods learn consensus clustering results but do not make full use of the distinct knowledge in each view so that they cannot well guarantee the complementarity across different views. In this paper, we propose a Distinction based Consensus Spectral Clustering (DCSC), which not only learns a consensus result
more » ... clustering, but also explicitly captures the distinct variance of each view. It is by using the distinct variance of each view that DCSC can learn a clearer consensus clustering result. In order to optimize the introduced optimization problem effectively, we develop a block coordinate descent algorithm which is theoretically guaranteed to converge. Experimental results on real-world data sets demonstrate the effectiveness of our method.
doi:10.1371/journal.pone.0208494 pmid:30521611 pmcid:PMC6283548 fatcat:6r752ff45fdwrgq5diz7r56qd4