用隐式正则化实现多标签特征选择，减少偏差并避免过拟合。
Implicit Regularization for Multi-label Feature Selection
- 通过哈达玛积参数化实现隐式正则化，无需显式惩罚项。
- 在多个基准数据集上表现更优，偏差更小且可能产生良性过拟合。
- 适合多标签学习中追求低偏差与鲁棒性的研究者。
本文针对多标签学习中的特征选择问题，提出一种基于隐式正则化与标签嵌入的新估计器。与使用 $l_{2,1}$-范数、MCP 或 SCAD 等显式正则化项的稀疏方法不同，本文采用哈达玛积参数化提供一种简洁替代方案。为引导特征选择过程，引入多标签信息的潜在语义表示作为标签嵌入。在若干已知基准数据集上的实验表明，所提估计器具有更小的额外偏差，可能引发良性过拟合。
原文摘要 · Abstract (English)
In this paper, we address the problem of feature selection in the context of multi-label learning, by using a new estimator based on implicit regularization and label embedding. Unlike the sparse feature selection methods that use a penalized estimator with explicit regularization terms such as $l_{2,1}$-norm, MCP or SCAD, we propose a simple alternative method via Hadamard product parameterization. In order to guide the feature selection process, a latent semantic of multi-label information method is adopted, as a label embedding. Experimental results on some known benchmark datasets suggest that the proposed estimator suffers much less from extra bias, and may lead to benign overfitting.
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