selproto
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, This file has been automatically generated. If badly readable, use the help-command in Matlab. |
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