AMCFCN: attentive multi-view contrastive fusion clustering net.

ABSTRACT: Advances in deep learning have propelled the evolution of multi-view clustering techniques, which strive to obtain a view-common representation from multi-view datasets. However, the contemporary multi-view clustering community confronts two prominent challenges. One is that view-specific representations lack guarantees to reduce noise introduction, and another is that the fusion process compromises view-specific representations, resulting in the inability to capture efficient information from multi-view data. This may negatively affect the accuracy of the clustering results. In this article, we introduce a novel technique named the "contrastive attentive strategy" to address the above problems. Our approach effectively extracts robust view-specific representations from multi-view data wit

SUBMITTER: Xiao H 

PROVIDER: S-EPMC11636696 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature