SPIDER The Spider Objects

KVQ object

   A=KVQ(H) returns a kvq object initialized with hyperparameters H.   
   The kvq object computes a learning vector quantization with guaranteed distortion 
   bounds. It does so by minimizing the L1 norm (parameter optmethod='l1lvq' / c.f. cited
   reference below) or an approximation of the L0 norm (parameter optmethod='l0arom').
   It also implements two variants of LVQ for labeled data: discriminative and shared
    LVQ (set parameter mode to 'discriminative' or 'shared' respectively). In 
   discriminative LVQ a vector can only become a codebook vector if it has at least 
   distance delta (parameter distd) to any examples of the other class. In mode shared an 
   example can only become a codebook vector if it has at most distance delta to a example
   of the opposite class. 
   Hyperparameters, and their defaults  
    dist=1;          -- allowed point to point distortion.
    distd=3			-- discriminative or shared radius
    child=distance         -- child stores the distance object.
    a.cutoff = 1./3.       -- cutoff value for importance values
    a.return_indices = 0   -- return indices of points instead of samples
    a.tol = 1e-6			  -- tolerance parameter for matlab's LINPROG linear program optimizer
    a.test_on_trainingset = 1 -- test the algorithm on the training data
   a.keep                -- kept data points -- store data points kept for model       
   a.alpha               -- importance factors
    train, test,circplot

Reference : A kernel approach vor vector quantization with guaranteed distortion bounds
Author : M. Tipping, B. Schölkopf
Link : ftp://ftp.research.microsoft.com/users/mtipping/aistats01.ps.gz