Complementary Classifier Induced Partial Label Learning

• URL: http://arxiv.org/abs/2305.09897v1
• Date: Wed, 17 May 2023 02:13:23 GMT
• Title: Complementary Classifier Induced Partial Label Learning
• Authors: Yuheng Jia, Chongjie Si, Min-ling Zhang
• Abstract summary: In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set. In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional classifier.
• Score: 54.61668156386079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set (a.k.a. complementary labels), which accurately indicates a set of labels that do not belong to a sample. In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional PLL classifier, to eliminate the false-positive labels in the candidate label set. Besides, we assume the feature space and the label space share the same local topological structure captured by a dynamic graph, and use it to assist disambiguation. Extensive experimental results validate the superiority of the proposed approach against state-of-the-art PLL methods on 4 controlled UCI data sets and 6 real-world data sets, and reveal the usefulness of complementary learning in PLL. The code has been released in the link https://github.com/Chongjie-Si/PL-CL.

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