Deep contrastive multi-view clustering with doubly enhanced commonality

Abstract

Recently, deep multi-view clustering leveraging autoencoders has garnered significant attention due to its ability to simultaneously enhance feature learning capabilities and optimize clustering outcomes. However, existing autoencoder-based deep multi-view clustering methods often exhibit a tendency to either overly emphasize view-specific information, thus neglecting shared information across views, or alternatively, to place undue focus on shared information, resulting in the dilution of complementary information from individual views. Given the principle that commonality resides within individuality, this paper proposes a staged training approach that comprises two phases: pre-training and fine-tuning. The pre-training phase primarily focuses on learning view-specific information, while the fine-tuning phase aims to doubly enhance commonality across views while maintaining these specific details. Specifically, we learn and extract the specific information of each view through the autoencoder in the pre-training stage. After entering the fine-tuning stage, we first initially enhance the commonality between independent specific views through the transformer layer, and then further strengthen these commonalities through contrastive learning on the semantic labels of each view, so as to obtain more accurate clustering results.