Partial Label Learning with Self-Guided Retraining

Authors

  • Lei Feng Nanyang Technological University
  • Bo An Nanyang Technological University

DOI:

https://doi.org/10.1609/aaai.v33i01.33013542

Abstract

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

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Published

2019-07-17

How to Cite

Feng, L., & An, B. (2019). Partial Label Learning with Self-Guided Retraining. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 3542-3549. https://doi.org/10.1609/aaai.v33i01.33013542

Issue

Section

AAAI Technical Track: Machine Learning