ImMC-CSFL: Imbalanced Multi-view Clustering Algorithm Based on Common-Specific Feature Learning

Xiaocui Li, Yu Xiao, Xinyu Zhang, Qingyu Shi, Xiance Tang

Abstract: Clustering as one of the main research methods in data mining, with the generation of multi-view data, multi-view clustering has become the research hotspot at present. Many excellent multi-view clustering algorithms have been proposed to solve various practical problems. These algorithms mainly achieve multi-view feature fusion by maximizing the consistency between views. However, in practical applications, multi-view data’ initial feature is often imbalanced, resulting in poor performance of existing multi-view clustering algorithms. Additionally, imbalanced multi-view data exhibits significant differences in feature across different views, which better reflects the complementarity of multi-view data. Therefore, it is important to fully extract feature from different views of imbalanced multi-view data. This paper proposes an imbalanced multi-view clustering algorithm based on common specific feature learning, ImMC-CSFL. Two deep networks are used to extract common and specific feature on each view, the GAN network is introduced to maximize the extraction of common feature from multi-view data, and orthogonal constraints are used to maximize the extraction of specific feature from different views. Finally, the learned imbalanced multi-view feature is input for clustering. The experiment result on three different multi-view datasets UCI Digits, BDGP, and CCV showed that our proposed algorithm had better clustering performance, and the effectiveness and robustness were verified through experiment analysis of different modules.