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Kernel PCA

version 1.0.0.0 (164 KB) by Bhartendu
Kernel PCA analysis with Kernel ridge regression & SVM regression

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Updated 26 May 2017

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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

Cite As

Bhartendu (2020). Kernel PCA (https://www.mathworks.com/matlabcentral/fileexchange/63130-kernel-pca), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (1)

sagar kumar dash

MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
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