Implicit Regularization for Multi-label Feature Selection

• URL: http://arxiv.org/abs/2411.11436v1
• Date: Mon, 18 Nov 2024 10:08:05 GMT
• Title: Implicit Regularization for Multi-label Feature Selection
• Authors: Dou El Kefel Mansouri, Khalid Benabdeslem, Seif-Eddine Benkabou,
• Abstract summary: 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. Experimental results on some known benchmark datasets suggest that the proposed estimator suffers much less from extra bias, and may lead to benign overfitting.
• Score: 1.5771347525430772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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