Partial Multi-Label Learning (PML) deals with a practical scenario where each instance is associated with a set of candidate labels, among which only a subset corresponds to the ground-truth labels while the others are unrelated. Existing PML methods typically employ label association recovery as a structural disambiguation strategy to identify credible labels. However, these methods attempt to recover associations directly from candidate labels, which leads to unreliable disambiguation due to the distorted label structures. To this end, this paper proposes a novel PML method via dual label association recovery (PML-DLAR).The essential strategy is to first eliminate the spurious correlations in label space before recovering dual label associations. Specifically, a biorthogonal transformation is employed to decouple the instance-level and label-level association structures affected by noisy labels. Subsequently, reliable instance-level associations are reconstructed through global geometric structure alignment between feature and pseudo-label spaces. Finally, class specific feature representations are constructed through class prototypes to guide label-level semantic association recovery in label space. Comprehensive experiments validate the superior performance of PML-DLAR over state-of-the-art methods.

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.