Applications and Implementation of Kernel Principal Component Analysis to Specific Data Sets
نویسنده
چکیده
Kernel Principal Component Analysis (KPCA) is a dimension reduction method that is closely related to Principal Component Analysis (PCA). This report gives an overview of kernel PCA and presents an implementation of the method in MATLAB. The implemented method is tested in a transductive setting on two data bases: Iris data and sugar data. Two methods for labeling data points are considered, the nearest neighbor method and kernel regression, together with some possible improvements of the methods.
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