Sparse PCA via matrix (2,1)-norm regularization with an application to feature selection
This paper is concerned with sparse PCA via the matrix (2,1)-norm regularization (PCA2,1). It can produce a row-sparse projection, a useful tool in machine learning when it comes to, for example, feature selection, that aims to choose most relevant features. Mathematically, PCA2,1 is a non-smooth op...
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| Autors principals: | , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Elsevier
2025-11-01
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| Col·lecció: | Results in Applied Mathematics |
| Matèries: | |
| Accés en línia: | http://www.sciencedirect.com/science/article/pii/S2590037425001402 |
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