CLASS: Deep Partial Label Feature Selection with Cluster-Guided Disambiguation and Structured Sparsity

Partial label learning efficiently extracts accurate labels from weak supervision, where each instance has multiple candidate labels but only one is correct. Existing partial label learning methods typically employ a two-stage process and fall into a suboptimal solution in high-dimensional settings, lacking an effective feedback mechanism between feature selection and label disambiguation. Nevertheless, existing methods suffer from at least one of two issues, i.e., redundant features and the uncertainty of label disambiguation. To address these problems, we propose a unified and deep partial label feature selection method with CLuster-guided disAmbiguation and Structured Sparsity (CLASS), which simultaneously preserve discriminative features and promotes candidate label disambiguation by bidirectional optimization. Specifically, we integrate nonlinear deep networks to capture high-order semantic relations and feature interactions, while employing a linear sparse gating mechanism to preserve the label-specific, discriminative and interpretable features. Moreover, a cluster-guided disambiguation module is designed to enforce inter-class global separation and intra-class local cohesion via distance constraint and confidence penalty and dynamically align predicted labels with the semantics of class prototypes. Extensive experimental results validate the superiority and effectiveness of our proposed CLASS over state-of-the-art methods.