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.

External IDs:dblp:conf/pakdd/LiXZST24