Complementary Classifier Induced Partial Label Learning
Yuheng Jia Chongjie Si Min-ling Zhang
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.