> 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.
>        Model
>         e_val                -- the eigenvectors
>         e_vec                -- the eigenvalues
>         dat                  -- training data (that we extracted from)

>        Methods:
>         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