An End-to-End Framework for Partial View-Aligned Clustering with Graph Structure

Abstract

Over the last decade, many multi-view clustering (MVC) methods have achieved promising results with intact and completely correct correspondence of multi-view data, which is hard to satisfy in practice leading to the problem of partially view-aligned clustering. In this paper, we propose a novel method to tackle it, termed An End-to-end Framework for Partial View-aligned Clustering with Graph structure(EGPVC). It employs Dykstra’s cyclic constraint projection algorithm to obtain the correspondence between two views. In particular, EGPVC develops an end-to-end framework for partially view-aligned clustering, in which representation learning and clustering process can benefit from each other through the deep embedded clustering layer. Moreover, a cross-view graph regularization term is designed to improve the quality of the learned common representation with graph structure information. Experimental results on several real-world datasets show our promising results comparing with the state-of-the-art methods in partially view-aligned clustering.