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Learning from Similarity-Confidence Data

Yuzhou Cao,Lei Feng,Yitian Xu,Bo An,Gang Niu,Masashi Sugiyama

Published 2021 in International Conference on Machine Learning

ABSTRACT

Weakly supervised learning has drawn considerable attention recently to reduce the expensive time and labor consumption of labeling massive data. In this paper, we investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data, where we aim to learn an effective binary classifier from only unlabeled data pairs equipped with confidence that illustrates their degree of similarity (two examples are similar if they belong to the same class). To solve this problem, we propose an unbiased estimator of the classification risk that can be calculated from only Sconf data and show that the estimation error bound achieves the optimal convergence rate. To alleviate potential overfitting when flexible models are used, we further employ a risk correction scheme on the proposed risk estimator. Experimental results demonstrate the effectiveness of the proposed methods.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    International Conference on Machine Learning

  • Publication date

    2021-02-13

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 2102.06879
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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