Summary of Implicit Regularization For Multi-label Feature Selection, by Dou El Kefel Mansouri and Khalid Benabdeslem and Seif-eddine Benkabou
Implicit Regularization for Multi-label Feature Selection
by Dou El Kefel Mansouri, Khalid Benabdeslem, Seif-Eddine Benkabou
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to feature selection in multi-label learning by using an implicit regularization-based estimator with label embedding. Unlike existing sparse methods, this method uses Hadamard product parameterization to simplify the process. The goal is to reduce bias and avoid overfitting. Experimental results on benchmark datasets show that the proposed estimator outperforms existing methods, suffering less from extra bias and potentially leading to benign overfitting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem in learning many labels at once by finding the most important features. Instead of using complex formulas, it proposes a simple new way to do this based on hidden patterns in the data. The method is tested on several datasets and shows that it’s better than other methods because it doesn’t introduce extra errors. |
Keywords
» Artificial intelligence » Embedding » Feature selection » Overfitting » Regularization
