Anchor Pseudo-Supervise Large-Scale Incomplete Multi-View Clustering

• Published in: IEEE access Vol. 11; pp. 107812 - 107822
• Main Authors: Zhu, Songbai, Dai, Jian, Yang, Guolai, Ren, Zhenwen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: anchor learning; Bipartite graph; Clustering; Clustering methods; Complexity theory; Data loss; Datasets; Design optimization; Graph theory; Incomplete multi-view clustering; Indexes; Learning; Learning systems; Social networking (online); Task analysis; tensor; Training
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3319564

In real life, only partial information of samples is available everywhere, this makes Incomplete multi-view clustering (IMVC) becomes a significant research topic to handle data loss situations. Recently, several methods leverage the anchor strategy by selecting fixed anchors to handle the challenging large-scale IMVC. However, all of them ignore the guidance of prior information hidden in the bipartite graph. Therefore, we propose a novel Anchor Pseudo-supervise Large-scale Incomplete Multi-view Clustering (AP-LIMC) method by introducing a prior indicator matrix as a pseudo-supervise anchor learning paradigm. Specifically, the prior indicator matrix is first introduced to control the distribution of anchors in each cluster. Then, an anchor pseudo-supervise learning framework is designed to generate high-quality anchors and a unified bipartite graph with prior indicator supervision. In addition, we design an optimized process with linear computational and extensive experiments on multiple public datasets with recent advances to validate the effectiveness, superiority, and efficiency. For example, on the Stl10 dataset, the performance of the proposed AP-LIMC improved by 23.95%,15.71%,27.39%, and 18.24% in terms of four evaluation metrics, respectively.