SPIDER The Spider Objects

KPCA object

   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.
    e_val                -- the eigenvectors  
    e_vec                -- the eigenvalues  
    dat                  -- training data (that we extracted from)  
    train, test  

Reference : Nonlinear component analysis as a kernel eigenvalue problem
Author : B. Schölkopf, A. Smola, and K.-R. Müller
Link : http://www.kernel-machines.org/papers/nlpca.ps.gz