SELPROTO

Select prototypes from dataset, generator mapping

   [P,I,J] = SELPROTO(A,N,TYPE,PAR,SEED)
   [P,I,J] = A*SELPROTO(N,TYPE,PAR,SEED)

Input
A	PRTools dataset or double matrix
N	Scalar, number of prototypes to be selected.  If N is a row vector with as many elements as A has classes,  the selection is done clas wise.  If 0 < N < 1, the corresponding fraction of A is selected.  Default is N = 1.
TYPE	Character string naming the algorithm (lower case supported):
'F'	or 'FFT', the Farthest First Traversal, see SELPROTF.
'W'	or 'WFT', the Worst First Traversal, see SELPROTF.
'K'	or 'KMEANS', the k-means algorithm. The nearest objects in A are returned. (default)
'M'	or 'MMEANS', the traditional k-means returning the cluster  means instead of their nearest objects. In I a NaN is returned.
'C'	or 'KCENTRES', the k-centres algorithm.
'R'	or 'RANDOM', random selection.
PAR	Initialisation: an index for an object in A or a character:
'R',	random selection.
'D',	deterministic selection (default). The object in A nearest  to the mean of A (default).
SEED	A desired state of random number generation applied to RANDRESET.

Output
P	PRTools dataset, or double matrix in case A is double,  containing the selected prototypes. If TYPE is 'M' these are not  objects from A and P is a double array.
I	The indices of the selected objects in A, P = A(I,:). I = NaN in  case TYPE is 'M'.
J	Indices of the not-selected objects. J = NaN in case TYPE is 'M'.

Description

This routine selects some possibly interesting objects, e.g. for building  a representation set from a feature representation. With an exception for  TYPE = 'M', objects from A are returned. In case PAR = 'D', the  procedures are deterministic (except for TYPE = 'R'): FFT starts with the  most remote object from the dataset mean. The KMEANS algorithms start  with the N objects selected by the FFT algorithm. KCENTRES has a greedy,  deterministic solution.

Example(s)

 % compute a dissimilarity based classifier for a representation set of
 % 10 objects using a Minkowski-1 distance.
 a = gendatb;
 u = selproto(10)*proxm('m',1)*fisherc;
 w = a*u;
 scatterd(a)
 plotc(w)

See also

datasets, mappings, gendat, randreset, prkmeans, kcentres, selprotf,