Learning from Similarity-Confidence Data. Cao, Y., Feng, L., Xu, Y., An, B., Niu, G., & Sugiyama, M. February, 2021. arXiv:2102.06879 [cs, stat]![Learning from Similarity-Confidence Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex 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 similarityconfidence (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.
@misc{cao_learning_2021,
title = {Learning from {Similarity}-{Confidence} {Data}},
url = {http://arxiv.org/abs/2102.06879},
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 similarityconfidence (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.},
language = {en},
urldate = {2023-11-06},
publisher = {arXiv},
author = {Cao, Yuzhou and Feng, Lei and Xu, Yitian and An, Bo and Niu, Gang and Sugiyama, Masashi},
month = feb,
year = {2021},
note = {arXiv:2102.06879 [cs, stat]},
keywords = {/unread, Computer Science - Machine Learning, Statistics - Machine Learning},
}
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