Tensorial Multi-View Clustering via Low-Rank Constrained High-Order Graph Learning

• Published in: IEEE transactions on circuits and systems for video technology Vol. 32; no. 8; pp. 5307 - 5318
• Main Authors: Jiang, Guangqi, Peng, Jinjia, Wang, Huibing, Mi, Zetian, Fu, Xianping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Consistency; Constraints; Correlation; Graphs; High-order graph learning; Laplace equations; low-rank constraint; Mathematical analysis; multi-view clustering; Optimization; Redundancy; Similarity; Task analysis; Tensors

Multi-view clustering aims to partition multi-view data into different categories by optimally exploring the consistency and complementary information from multiple sources. However, most existing multi-view clustering algorithms heavily rely on the similarity graphs from respective views and fail t...