- Tytuł:
- Methods of Representation for Kernel Canonical Correlation Analysis
- Autorzy:
-
Krzyśko, Mirosław
Waszak, Łukasz - Powiązania:
- https://bibliotekanauki.pl/articles/465909.pdf
- Data publikacji:
- 2012
- Wydawca:
- Główny Urząd Statystyczny
- Tematy:
- Canonical correlation analysis generalized eigenvalue problem reproducing kernel Hilbert space
- Opis:
- Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation. This problem is equivalent to solving the generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we construct nonlinear canonical correlation analysis in reproducing kernel Hilbert spaces. The new kernel generalized eigenvalue problem always has the solution equal to one, and this is a typical case of over-fitting. We present methods to solve this problem and compare the results obtained by classical and kernel canonical correlation analysis.
- Źródło:
-
Statistics in Transition new series; 2012, 13, 2; 301-310
1234-7655 - Pojawia się w:
- Statistics in Transition new series
- Dostawca treści:
- Biblioteka Nauki
Artykuł