Graph-Driven deep Multi-View Clustering with self-paced learning

Abstract

Multi-view clustering has attracted widespread attention because it can improve clustering performance by integrating information from various views of samples. However, many existing methods either neglect graph information entirely or only partially incorporate it, leading to information loss and non-comprehensive representation. Besides, they usually make use of graph information by determining a fixed number of neighbors based on prior knowledge, which limits the exploration of graph information contained in data. To address these issues, we propose a novel method, termed Graph-Driven deep Multi-View Clustering with self-paced learning (GDMVC), which integrates both feature information and graph information to better explore information within the data. Additionally, based on the idea of self-paced learning, this method gradually increases the number of neighbors and updates the similarity matrix, progressively providing more graph information to guide representation learning. By this way, we avoid issues associated with a fixed number of neighbors and ensure a thorough exploration of graph information contained in the original data. Furthermore, this method not only ensures the consistency among views but also leverages graph information to further enhance the unified representation, aiming to obtain more separable cluster structures. Extensive experiments on real datasets demonstrate its effectiveness for multi-view clustering. Our code will be released on https://github.com/yff-java/GDMVC/.