Confidence-Aware With Prototype Alignment for Partial Multi-label Learning

Sixian Chan (Zhejiang University of Technology) · Guoxu Zhou (Guangdong University of Technology) · Jie Wen (Harbin Institute of Technology, Shenzhen) · Yu Chen (Shanghai Jiaotong University) · Weijun Lv (Guangdong University of Technology) · Xiaozhao Fang · Xuhuan Zhu (Guangdong University of Technology)

candidate label setclass prototypesclassifier parametersfuzzy clusteringfuzzy membershipslabel confidencelabel prototype learninglabel reliability indicator matrixlabel semanticsnoise interferencepartial multi-label learningperformance advantagesprototype-based methodsrobust multi-label classifierstructured representationstransformation matrices

Label prototype learning has emerged as an effective paradigm in Partial Multi-Label Learning (PML), providing a distinctive framework for modeling structured representations of label semantics while naturally filtering noise through prototype-based label confidence estimation. However, existing prototype-based methods face a critical limitation: class prototypes are the biased estimates due to noisy candidate labels, particularly when positive samples are scarce. To this end, we first propose a mutually class prototype alignment strategy bypassing noise interference by introducing two different transformation matrices, which makes the class prototypes learned by the fuzzy clustering and candidate label set mutually alignment for correcting themselves. Such alignment is also passed on to the fuzzy memberships label in turn. In addition, to eliminate noise interference in the candidate label set during the classifier learning, we use the learned permutation matrix to transform the fuzzy memberships label for learning a label reliability indicator matrix accompanied by the candidate label set. This makes the label reliability indicator matrix absolutely prevent the occurrence of numerical values located in non-label and simultaneously eliminate the introduction of incorrect label as much as possible. The resulting indicator matrix guides a robust multi-label classifier training process, jointly optimizing label confidence and classifier parameters. Extensive experiments demonstrate that our proposed model exhibits significant performance advantages over state-of-the-art PML approaches.