Partial Multi-View Clustering via Self-Supervised Network

Partial multi-view clustering is a challenging and practical research problem for data analysis in real-world applications, due to the potential data missing issue in different views. However, most existing methods have not fully explored the correlation information among various incomplete views. In addition, these existing clustering methods always ignore discovering discriminative features inside the data itself in this unsupervised task. To tackle these challenges, we propose Partial Multi-View Clustering via Self-Supervised \textbf{N}etwork (PVC-SSN) in this paper. Specifically, we employ contrastive learning to obtain a more discriminative and consistent subspace representation, which is guided by a self-supervised module. Self-supervised learning can exploit effective cluster information through the data itself to guide the learning process of clustering tasks. Thus, it can pull together embedding features from the same cluster and push apart these from different clusters. Extensive experiments on several benchmark datasets show that the proposed PVC-SCN method outperforms several state-of-the-art clustering methods.

Authors

• Wei Feng Xi'an Jiaotong University
• Guoshuai Sheng School of Telecommunications Engineering, Xidian University, Xi'an, Shaanxi, China, 710071
• Qianqian Wang Xidian University
• Quanxue Gao Xidian University
• Zhiqiang Tao Rochester Institute of Technology
• Bo Dong Xi'an Jiaotong University

Keywords

• ML: Clustering
• ML: Deep Learning Algorithms
• ML: Deep Learning Theory
• ML: Multi-class/Multi-label Learning & Extreme Classification
• ML: Multi-instance/Multi-view Learning
• ML: Multimodal Learning
• ML: Representation Learning