- Tytuł:
- Supervised Kernel Principal Component Analysis by Most Expressive Feature Reordering
- Autorzy:
-
Ślot, K.
Adamiak, K.
Duch, P.
Żurek, D. - Powiązania:
- https://bibliotekanauki.pl/articles/308598.pdf
- Data publikacji:
- 2015
- Wydawca:
- Instytut Łączności - Państwowy Instytut Badawczy
- Tematy:
-
feature selection
kernel methods
pattern classification - Opis:
- The presented paper is concerned with feature space derivation through feature selection. The selection is performed on results of kernel Principal Component Analysis (kPCA) of input data samples. Several criteria that drive feature selection process are introduced and their performance is assessed and compared against the reference approach, which is a combination of kPCA and most expressive feature reordering based on the Fisher linear discriminant criterion. It has been shown that some of the proposed modifications result in generating feature spaces with noticeably better (at the level of approximately 4%) class discrimination properties.
- Źródło:
-
Journal of Telecommunications and Information Technology; 2015, 2; 3-10
1509-4553
1899-8852 - Pojawia się w:
- Journal of Telecommunications and Information Technology
- Dostawca treści:
- Biblioteka Nauki
Artykuł