Ambiguous Instance-Aware Contrastive Network with Multi-Level Matching for Multi-View Document Clustering

Abstract

Multi-view document clustering (MvDC) aims to improve the accuracy and robustness of clustering by fully considering the complementarity of different views. However, in real-world clustering applications, most existing works suffer from the following challenges: 1) They primarily align multi-view data based on a single perspective, such as features and classes, thus ignoring the diversity and comprehensiveness of representations. 2) They treat each instance equally in cross-view contrastive learning without considering ambiguous ones, which weakens the model's discriminative ability. To address these problems, we propose an ambiguous instance-aware contrastive network with multi-level matching (AICN-MLM) for MvDC tasks. This model contains two key modules: a multi-level matching module and an ambiguous instance-aware contrastive learning module. The former attempts to align multi-view data from different perspectives, including features, pseudo-labels, and prototypes. The latter dynamically adjusts instance weights through a weight modulation function to highlight ambiguous instance pairs. Thus, our proposed method can effectively explore the consistency of multi-view document data and focus on ambiguous instances to enhance the model's discriminative ability. Extensive experimental results on several multi-view document datasets verify the effectiveness of our proposed method.