{"type":"ScholarlyArticle","title":"Label Confidence Recovery with High-order Label Correlation in Partial Multi-label Learning","authors":[{"name":"Yuanchao Dai","slug":"yuanchao-dai","given":"Yuanchao","family":"Dai","affiliation":""},{"name":"Ximing Li","slug":"ximing-li","given":"Ximing","family":"Li","affiliation":""},{"name":"Ming-Kun Xie","slug":"ming-kun-xie","given":"Ming-Kun","family":"Xie","affiliation":""},{"name":"Xurui Li","slug":"xurui-li","given":"Xurui","family":"Li","affiliation":""},{"name":"Changchun Li","slug":"changchun-li","given":"Changchun","family":"Li","affiliation":""}],"abstract":"Partial multi-label learning (PML) addresses weakly-supervised scenarios where each instance is associated with a candidate label set containing both ground-truth and noisy labels. Existing PML methods primarily focus on instance-level features or pairwise label correlations for disambiguation. Building on recent insights that exploiting pairwise label correlations improves disambiguation, we observe that high-order label correlations provide even stronger disambiguation evidence in partial settings because ground-truth labels with high-order correlations frequently co-occur, while noise labels produce inconsistent combinations that rarely repeat. To exploit this property, we propose REHC-PML (Label Confidence REcovery with High-order Label Correlations in Partial Multi-label Learning), which mines frequent high-order co-occurrences from candidate sets to identify global high-order label correlations, selects instance-relevant correlations via Gumbel-Softmax pruning, and propagates their evidence to constituent labels for confidence recovery. Self-training iteratively refines pseudo-labels and trains the classifier. Extensive experiments on both real-world and UCI datasets demonstrate the effectiveness of REHC-PML.","doi":"10.24963/ijcai.2026/472","url":"https://ijcai.org/proceedings/2026/472","pdf":"https://ijcai.org/proceedings/2026/0472.pdf","markdown":"https://ijcai.org/proceedings/2026/472.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/472","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":472,"pages":{"first":4242,"last":4250},"keywords":["Machine Learning: Weakly supervised learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}