Towards Enabling Binary Decomposition for Partial Multi-Label Learning

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. PP; no. 11; pp. 1 - 16
• Main Authors: Liu, Bing-Qing, Jia, Bin-Bin, Zhang, Min-Ling
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adequacy; binary decomposition; Codes; Coding; Comparative studies; Decoding; Decomposition; Encoding; Error correction; error-correcting output codes; Estimation; Iterative methods; Labeling; Labels; Machine learning; partial multi-label learning; Performance prediction; Phase locked loops; Prediction models; State-of-the-art reviews; Supervised learning; Training

Partial multi-label learning (PML) is an emerging weakly supervised learning framework, where each training example is associated with multiple candidate labels which are only partially valid. To learn the multi-label predictive model from PML training examples, most existing approaches work by iden...