arXiv 2206.005172022-06cs.LG

一个正标签就足够了：带标签增强的单正多标签学习

One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement

Southeast University（东南大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所高级智能项目中心）

Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang

备注：: Accepted to NeurIPS 2022

摘要：

多标签学习（MLL）同时从每个与多个标签相关联的示例中学习，其中为每个训练样本注释所有相关标签的高成本对于现实世界的应用是具有挑战性的。为了应对这一挑战，我们研究了单正多标签学习（SPMLL），其中每个示例仅用一个相关标签进行注释，并表明可以成功地学习理论上接地的多标签分类器。本文提出了一种新的SPMLL方法SMILE，即，提出了一种带标签增强的单正多标签学习算法。具体地说，一个无偏的风险估计，可以保证近似收敛到最优的风险最小化的全监督学习，并表明，一个积极的标签，每个实例是足够的训练的预测模型。然后，通过将潜在软标签的恢复过程作为标签增强过程，建立了相应的经验风险估计器，其中潜在软标签的后验密度近似于由推理模型参数化的变分Beta密度.在基准数据集上的实验验证了该方法的有效性。

英文摘要：

Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training example is challenging for real-world applications. To cope with the challenge, we investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label, and show that one can successfully learn a theoretically grounded multi-label classifier for the problem. In this paper, a novel SPMLL method named SMILE, i.e., Single-positive MultI-label learning with Label Enhancement, is proposed. Specifically, an unbiased risk estimator is derived, which could be guaranteed to approximately converge to the optimal risk minimizer of fully supervised learning and shows that one positive label of each instance is sufficient to train the predictive model. Then, the corresponding empirical risk estimator is established via recovering the latent soft label as a label enhancement process, where the posterior density of the latent soft labels is approximate to the variational Beta density parameterized by an inference model. Experiments on benchmark datasets validate the effectiveness of the proposed method.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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