Related papers: Learning from Similarity-Confidence Data

Learning from Similarity-Confidence Data

URL: http://arxiv.org/abs/2102.06879v1
Date: Sat, 13 Feb 2021 07:31:16 GMT
Title: Learning from Similarity-Confidence Data
Authors: Yuzhou Cao, Lei Feng, Yitian Xu, Bo An, Gang Niu, Masashi Sugiyama
Abstract summary: We investigate a novel weakly supervised learning problem of learning from similarity-confidence (Sconf) data. 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.
Score: 94.94650350944377
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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