> The Spider Objects

>            SPECTRAL object

>        A=SPECTRAL(H) returns a spectral clustering object
>        initialized with hyperparameters H.

>        Structure of a spectral object:

>        Parameters:

>          child=distance  -- distance measure
>          sigma=[]        -- controls the scale of exponential
>                             if sigma is set to a number, no research is done.
>                             Otherwise, the distortion is minimized wrt. sigma

>          k=2             -- number of desired cluster

>        Model

>          d                -- training set containing the inputs as well as
>                              the clusters (in the Y field)

>        Methods:
>          training: cluster a dataset
>          testing:  assign points to clusters according to the
>                    cluster of the nearest point

>        Description:

>          The object is trained via an eigenvalue decomposition of a matrix derived from
>          its affinity matrix K:
>                  K(i,j) = exp(-d(x_i,x_j)/(2sigma^2));

>          The lines of the k largest eigenvectors are normalized and are clustered with
>          k-means. The choice of sigma is optimized to minimize the distortion of this k-means
>          clustering.
>        Example :
>          plot(d.X(:,1),d.X(:,2),'r.');
>          ['Press Key']
>          pause;
>          d=gen(spiral({'m=200','n=0.5','noise=0.35'}));
>          [r,a]=train(spectral('sigma=0.1'),d);
>          I=find(r.X==1);clf;hold on;
>          plot(d.X(I,1),d.X(I,2),'r.');
>          I=find(r.X==2);
>          plot(d.X(I,1),d.X(I,2),'b.');

> | 1. Reference : On Spectral Clustering: Analysis and an algorithm

> | Author : Andrew Y. Ng, Michael I. Jordan, Yair Weiss

> | 1. Link : http://citeseer.ist.psu.edu/ng01spectral.html

> | 2. Reference : Spectral kernel methods for clustering. In Neural Information Processing Systems

> | Author : Christianini, J. Shawe-Taylor, and J. Kandola.

> | 2. Link : http://citeseer.ist.psu.edu/context/2076443/0