2026· Computers, Materials & Continua· 0 citations· 56 references
TL;DR
This work proposes an integrated learning paradigm that simultaneously enhances feature compactness and improves robustness against label noise and introduces a feature disentanglement mechanism that isolates reliable label-related feature representations from spurious ones introduced by noisy supervision.
Abstract
: Partial multi-label learning addresses scenarios where each instance is associated with a set of candidate labels that include both relevant and irrelevant ones. In practical scenarios, such label sets are often simultaneously incomplete and noisy, which severely hampers the ability of models to extract compact and discriminative features. To address these issues, we propose an integrated learning paradigm that simultaneously enhances feature compactness and improves robustness against label noise. Our method learns an adaptive fuzzy neighborhood graph to capture the intrinsic relationships among instances. The resulting graph enables reliable label propagation, which effectively rectifies incorrect annotations and infers missing labels. In addition, we introduce a feature disentanglement mechanism that isolates reliable label-related feature representations from spurious ones introduced by noisy supervision. By integrating feature learning and label refinement into a joint optimization process, the proposed approach achieves a synergistic improvement in both representation quality and label reliability. Extensive theoretical analysis and empirical studies on multiple benchmark datasets demonstrate that our framework consistently outperforms state-of-the-art methods in terms of accuracy, stability, and robustness to annotation noise.
A novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (Wpml3cp), solved by the gradient descent with an augmented Lagrange multiplier technique, and empirical results demonstrate that Wpml3cp and Wpml3cp-D can outperform the PML baselines in various noisy levels.
Ximing Li, Yuanchao Dai, Bing Wang et al.· ACM Transactions on Knowledg...· 0 citations
This paper proposes a novel Partial label-based Self-training framework (PaSta) that leverages partial label learning technique to overcome the limitations of existing methods and designs a partial label-based classification model with two well-crafted loss functions to guide the model learning at both label and representation spaces.
Yujing Liu, Yixin Liu, Yu Zheng et al.· 0 citations
Instance-wise feature selection (IWFS) identifies informative features for each instance, improving generalization by discarding irrelevant information and enhancing interpretability through personalized explanations. Most IWFS methods adopt a selector--predictor architecture, where a selector generates instance-specific masks to guide prediction. This often leads to co-adaptation, in which the selector encodes label information into the mask, resulting in spurious correlations and unfaithful explanations. Existing methods also struggle to capture diverse local patterns, which is critical for IWFS under heterogeneous sparsity. We propose VIBMask, a unified IWFS framework by the variational information bottleneck. VIBMask mitigates co-adaptation by penalizing mutual information between unselected features and the label, and improves expressivity via an ensemble of diverse selectors that capture heterogeneous sparse patterns. We further derive a novel variational lower bound for discrete masks, enabling efficient end-to-end training through reparameterization. Experiments on synthetic and real datasets show that VIBMask consistently outperforms state-of-the-art IWFS methods in both predictive accuracy and informative feature discovery.
Lu Sun, Jun Sakuma· Proceedings of the Thirty-Fi...· 0 citations
Multi-label data often contain high-dimensional features, outlier instances, and noisy labels, all of which can lead to the curse of dimensionality and decreased performance in downstream tasks. Although numerous data reduction methods have been developed, existing approaches face two major limitations: 1) existing methods typically select features, instances, or labels independently, without considering how noise or redundancy in one dimension may negatively influence the selection of others; 2) there are very few feature and instance co-selection methods that commonly assume label annotations are free of noise, which is seldom true in practice. To address these issues, we propose Evidential Multi-Label Multi-Dimensional Selection (EMMS), which jointly performs feature, instance, and label selection on multi-label data. EMMS introduces a dual projection mechanism with sparsity constraints that transforms high-dimensional data first into a latent space and then into the label space. Simultaneously, projection residuals are explicitly modeled to facilitate the identification of representative instances, enabling unified selection across features, instances, and labels. Moreover, EMMS employs evidence theory to fuse instance-level and label-level evidence, thereby enhancing the reliability of the learned labels and reducing the influence of noisy labels, which in turn promotes multi-dimensional selection. Extensive experiments demonstrate that EMMS consistently outperforms state-of-the-art methods.
Li Yang, Yan-Yong Huang, Jin-Yuan Chang et al.· Proceedings of the Thirty-Fi...· 0 citations
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