An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915173373050880 |
|---|---|
| author | Wang, Li Zhang, Lei-Hong Li, Ren-Cang |
| author_facet | Wang, Li Zhang, Lei-Hong Li, Ren-Cang |
| contents | A novel feature selection model via orthogonal canonical correlation analysis with the $(2,1)$-norm regularization is proposed, and the model is solved by a practical NEPv approach (nonlinear eigenvalue problem with eigenvector dependency), yielding a feature selection method named OCCA-FS. It is proved that OCCA-FS always produces a sequence of approximations with monotonic objective values and is globally convergent. Extensive numerical experiments are performed to compare OCCA-FS against existing feature selection methods. The numerical results demonstrate that OCCA-FS produces superior classification performance and often comes out on the top among all feature selection methods in comparison. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization Wang, Li Zhang, Lei-Hong Li, Ren-Cang Numerical Analysis Optimization and Control 58C40, 65F30, 65H17, 65K05, 90C26 A novel feature selection model via orthogonal canonical correlation analysis with the $(2,1)$-norm regularization is proposed, and the model is solved by a practical NEPv approach (nonlinear eigenvalue problem with eigenvector dependency), yielding a feature selection method named OCCA-FS. It is proved that OCCA-FS always produces a sequence of approximations with monotonic objective values and is globally convergent. Extensive numerical experiments are performed to compare OCCA-FS against existing feature selection methods. The numerical results demonstrate that OCCA-FS produces superior classification performance and often comes out on the top among all feature selection methods in comparison. |
| title | An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization |
| topic | Numerical Analysis Optimization and Control 58C40, 65F30, 65H17, 65K05, 90C26 |
| url | https://arxiv.org/abs/2502.18633 |