Research on the selection method of principal components in KPCA based on AIC and BIC
Kernel principal component analysis (KPCA) is an unsupervised feature extraction method widely used for nonlinear dimensionality reduction. However, in practical applications, the selection of the number of principal components often faces significant subjectivity and computational costs. Existing m...
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| Autors principals: | , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Academic Journals Center of Shanghai Normal University
2025-12-01
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| Col·lecció: | 上海师范大学学报. 自然科学版 |
| Matèries: | |
| Accés en línia: | http://shnu.ijournals.cn/zrkxb/shsfqkszrb/ch/reader/view_abstract.aspx?file_no=20250605 |
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