Implicit Regularization for Multi-label Feature Selection

2024/11/18 by Dou El Kefel Mansouri, Mansouri, Dou El Kefel, Khalid Benabdeslem +3
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Face and Expression Recognition #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Text and Document Classification Technologies

paper · pdf · doi:10.48550/arxiv.2411.11436

openalex publication_date 2024/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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