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
• ISSN: 0162-8828; 1939-3539; 2160-9292; 1939-3539
• DOI: 10.1109/TPAMI.2023.3290797

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 identifying valid labels within candidate label set via label confidence estimation. In this paper, a novel strategy towards partial multi-label learning is proposed by enabling binary decomposition for handling PML training examples. Specifically, the widely used error-correcting output codes (ECOC) techniques are adapted to transform the PML learning problem into a number of binary learning problems, which refrains from using the error-prone procedure of estimating labeling confidence of individual candidate label. In the encoding phase, a ternary encoding scheme is utilized to balance the definiteness and adequacy of the derived binary training set. In the decoding phase, a loss weighted scheme is applied to consider the empirical performance and predictive margin of derived binary classifiers. Extensive comparative studies against state-of-the-art PML learning approaches clearly show the performance advantage of the proposed binary decomposition strategy for partial multi-label learning.