A survey on representation learning for multi-view data

Abstract

Multi-view clustering has become a rapidly growing field in machine learning and data mining areas by combining useful information from different views for last decades. Although there have been some surveys based on multi-view clustering, most of these works ignore simultaneously taking the self-supervised and non-self supervised multi-view clustering into consideration. We give a novel survey for sorting out the existing algorithms of multi-view clustering in this work, which can be classified into two different categories, i.e., non-self supervised and self-supervised multi-view clustering. We first review the representative approaches based on the non-self supervised multi-view clustering, which consist of methods based on non-representation learning and representation learning. Furthermore, the methods built on non-representation learning contain works based on matrix factorization, kernel and other non-representation learning. Methods based on representation learning consist of multi-view graph clustering, deep representation learning and multi-view subspace clustering. For the methods based on self-supervised multi-view clustering, we divide them into contrastive methods and generative methods. Overall, this survey attempts to give an insightful overview regarding the developments in the multi-view clustering field.