A Consensus Clustering Algorithm for Multitask Multiview Learning

摘要

Multitask multiview clustering involves multitask algorithms and multiview algorithms in clustering. As there exists certain relationship among multiple tasks and abundant features in various views, multitask multiview clustering utilizing latent structures to promote the performance for single task, has received much attention recently. We propose a consensus clustering method in this paper for multitask multiview situation $(C^{2} {MTMV})$. It firstly integrates the features from various views to produce a consistent representation for each task. Then it further explores the knowledge existing in within-task and between-tasks and transfers them into other related tasks to assist in clustering. Experimental results comparing with 6 existing algorithms on 5 datasets show the superiority of our method.