> The Spider Objects

>       PPCA kpca object - Probabilistic Principal Components Analysis

>        A=PPCA(H) returns a ppca object initialized with hyperparameters H.

>        Hyperparameters, and their defaults
>         feat=0;              -- number of features, default 0 means all via dim X
>         iterations=10000     -- number of iterations for the EM algorithm

>        Model
>         e_val                -- the eigenvalues
>         e_vec                -- the eigenvectors
>         W                    -- the principal components
>         offset               -- the mean of the training data
>         sigma                -- the isotropic noise term sigma^2
>         dat                  -- training data (that we extracted from)

>        Methods:
>         train, test

>        Example:
>        d=gen(toy({'l=1000','n=2'}));
>        d.X=d.X*[1,0.1;0.2,0.2];
>        [r,a]=train(ppca('iterations=1000'),d);
>        plot(d.X(:,1),d.X(:,2),'r.');
>        hold on;
>        line([0,a.e_vec(1,1)],[0,a.e_vec(2,1)])
>        line([0,a.e_vec(1,2)],[0,a.e_vec(2,2)])
>        axis([-1,1,-1,1])

> | Reference : Probabilistic analysis of kernel principal components : mixture modeling and classification

> | Author : Shaohua Zhou

> | Link : http://www.cfar.umd.edu/~shaohua/papers/zhou04tpami.pdf