Multi-Label Classification With High-Rank and High-Order Label Correlations

• Published in: IEEE transactions on knowledge and data engineering Vol. 36; no. 8; pp. 4076 - 4088
• Main Authors: Si, Chongjie, Jia, Yuheng, Wang, Ran, Zhang, Min-Ling, Feng, Yanghe, Qu, Chongxiao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classification; Classification algorithms; Comparative studies; Correlation; Effectiveness; Factorization; high-order label correlations; High-rank matrix approximation; Labels; Machine learning; Matrix decomposition; multi-label classification; Parameter estimation; Semantics; Silicon; Task analysis

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate f...