Jointly Deep Multi-View Learning for Clustering Analysis

19 Aug 2018Bingqian LinYuan XieYanyun QuCuihua LiXiaodan Liang

In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignments can be learned simultaneously. Our key idea is that the joint learning strategy can sufficiently exploit clustering-friendly multi-view features and useful multi-view complementary information to improve the clustering performance... (read more)

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