Learning with multiple complementary labels

Lei Feng*, Takuo Kaneko, Bo HAN, Gang Niu, Bo An, Masashi Sugiyama

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

A complementary label (CL) simply indicates an incorrect class of an example, but learning with CLs results in multi-class classifiers that can predict the correct class. Unfortunately, the problem setting only allows a single CL for each example, which notably limits its potential since our labelers may easily identify multiple CLs (MCLs) to one example. In this paper, we propose a novel problem setting to allow MCLs for each example and two ways for learning with MCLs. In the first way, we design two wrappers that decompose MCLs into many single CLs, so that we could use any method for learning with CLs. However, the supervision information that MCLs hold is conceptually diluted after decomposition. Thus, in the second way, we derive an unbiased risk estimator; minimizing it processes each set of MCLs as a whole and possesses an estimation error bound. We further improve the second way into minimizing properly chosen upper bounds. Experiments show that the former way works well for learning with MCLs but the latter is even better.

Original languageEnglish
Title of host publication37th International Conference on Machine Learning, ICML 2020
EditorsHal Daume, Aarti Singh
PublisherInternational Machine Learning Society (IMLS)
Pages3053-3062
Number of pages10
ISBN (Electronic)9781713821120
Publication statusPublished - 2020
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 13 Jul 202018 Jul 2020

Publication series

Name37th International Conference on Machine Learning, ICML 2020
VolumePartF168147-4

Conference

Conference37th International Conference on Machine Learning, ICML 2020
CityVirtual, Online
Period13/07/2018/07/20

Scopus Subject Areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

Fingerprint

Dive into the research topics of 'Learning with multiple complementary labels'. Together they form a unique fingerprint.

Cite this