Partial label learning via weighted centroid clustering disambiguation

• Published in: Neurocomputing (Amsterdam) 2024-11, Vol.604, p.128312, Article 128312
• Main Authors: Tian, Yuhang, Niu, Xin, Chai, Jing
• Format: Article
• Language: English
• Subjects: Local similarity; Partial label learning; Per-class global information; Weighted centroid clustering
• ISSN: 0925-2312
• DOI: 10.1016/j.neucom.2024.128312

Partial Label Learning (PLL) is a weakly supervised learning problem that induces a multi-class classifier from data with candidate labels, among which only one is the ground-truth label. The crucial challenge in PLL is to disambiguate the false-positive labels from the candidate labels. However, most existing PLL methods fail to simultaneously consider both the instance-level similarity and the class-level information during label disambiguation. In this paper, we propose a novel two-stage method based on weighted centroid clustering, which efficiently utilizes both the local similarity among instances and the per-class global information. In the first stage, we initialize the center of each class using label propagation based on the instance-level similarity, and obtain the disambiguated instances via weighted centroid clustering derived from the per-class global information. In the second stage, the disambiguated instances are used to train a multi-class classifier. Extensive experiments on both controlled UCI datasets and real-world datasets show the superiority of the proposed method in classification accuracy.