A=KPCA(H) returns a kpca object initialized with hyperparameters H.
Hyperparameters, and their defaults
feat=0; -- number of features, default 0 means all via rank(K)
center_data=1; -- if data is to be centered in feature space
child=linear -- child stores the kernel. Default is the linear
kernel and therefore normal pca.
NOTE: This has changed. The old version was
assuming a kernel matrix as data. In order to
simulate the old behaviour use custom kernel.
Model
e_val -- the eigenvectors
e_vec -- the eigenvalues
dat -- training data (that we extracted from)
Methods:
train, test
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Reference : Nonlinear component analysis as a kernel eigenvalue problem |
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Author : B. Schölkopf, A. Smola, and K.-R. Müller |