Multi-view Contrastive Clustering with Clustering Guidance and Adaptive Auto-encoders

Abstract

Graph-based clustering plays an important role in the clustering area. However, in general clustering tasks, the graph structure of data does not exist, so the strategy for constructing the graph is crucial for the performance of the subsequent tasks. In the subsequent comparison task, existing methods fail to consider the class information and will introduce false-negative samples in the random negative sampling, causing poor performance. To this end, we propose a multi-view comparison clustering framework based on clustering guidance and adaptive encoder. First, the graph is constructed adaptively according to the generative perspective of the graphs. The adaptive process is designed to induce the model to exploit the high-level information behind data and utilize the non-Euclidean structure. Then, representations can be optimized by aligning with clustered class information, and simultaneously, the optimized representations can promote clustering, leading to more powerful representations and clustering results. Extensive experiments on five datasets demonstrate that our method achieves new state-of-the-art results on clustering tasks.