Kernel PCA

Kernel PCA analysis with Kernel ridge regression & SVM regression
990 descargas
Actualizado 26 may 2017

Ver licencia

Refer to 6.2.1 KPCA, Kernel Methods for Pattern Analysis, John Shawe-Taylor University of Southampton, Nello Cristianini University of California at Davis
Refer to 6.2.2 Kernel Ridge Regression, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Nello Cristianini and John Shawe-Taylor

Kernel PCA:
Kernel PCA is the application of PCA in a kernel-defined feature space making use of the dual representation.
http://pca.narod.ru/scholkopf_kernel.pdf

Reference: (for SVR) https://in.mathworks.com/matlabcentral/fileexchange/63060-support-vector-regression Reference: (for Ridge regression)https://in.mathworks.com/matlabcentral/fileexchange/63122-kernel-ridge-regression

Citar como

Bhartendu (2024). Kernel PCA (https://www.mathworks.com/matlabcentral/fileexchange/63130-kernel-pca), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2016a
Compatible con cualquier versión
Compatibilidad con las plataformas
Windows macOS Linux
Categorías
Más información sobre Dimensionality Reduction and Feature Extraction en Help Center y MATLAB Answers.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
Versión Publicado Notas de la versión
1.0.0.0