Description: KPCA is used to reduce the dimension of data, first through the kernel method, the sample is mapped to high-dimensional space, and then the principal component analysis process.
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File list (Check if you may need any files):
kPCA_v3.1\1207.3538.pdf
kPCA_v3.1\code\PCA.m
kPCA_v3.1\code\distanceMatrix.m
kPCA_v3.1\code\kPCA.m
kPCA_v3.1\code\kPCA_NewData.m
kPCA_v3.1\code\kPCA_PreImage.m
kPCA_v3.1\code\kernel.m
kPCA_v3.1\code\kernel_NewData.m
kPCA_v3.1\demo1\SyntheticData.mat
kPCA_v3.1\demo1\demo_SyntheticData.m
kPCA_v3.1\demo2\YaleFaceData.mat
kPCA_v3.1\demo2\demo_YaleFace.m
kPCA_v3.1\demo3\demo_faceASM_PCA.m
kPCA_v3.1\demo3\demo_faceASM_kPCA.m
kPCA_v3.1\demo3\drawFaceModel.m
kPCA_v3.1\demo3\points_20.mat
license.txt
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