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FDSC
W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
W = A*FDSC([],R,FEATMAP,TYPE,P,CLASSF)
D = X*W
| Input | |
| A | Dateset used for training |
| R | Dataset used for representation or a fraction of A to be used for this. Default: R = A. |
| FEATMAP | Preprocessing in feature space (e.g. SCALEM) Default: no preprocessing. |
| TYPE | Dissimilarity rule, see PROXM Default 'DISTANCE'. |
| P | Parameter for dissimilarity rule, see PROXM Default P = 1. |
| CLASSF | Classifier used in dissimilarity space Default LIBSVC([],[],100) |
| X | Test set |
| Output | |
| W | Resulting, trained feature space classifier |
| D | Classification matrix |
This routine builds a classifier in feature space based on a dissimilarity representation defined by the representation set R and the dissimilarities found by A*FEATMAP*PROXM(R*FEATMAP,TYPE,P). FEATMAP is a preprocessing in feature space, e.g. scaling (SCALEM([],'variance') or pre-whitening (KLMS).
R can either be explicitely given, or by a fraction of A. In the latter case the part of A that is randomly generated to create the representation set R is excluded from the training set.
New objects in feature space can be classified by D = B*W or by D = MAP(B,W). Labels can be found by LAB = D*LABELD or LAB = LABELD(D).
A = GENDATB([100 100]); % Training set of 200 objects
R = GENDATB([10 10]); % Representation set of 20 objects
W = FDSC(A,R); % Compute classifier
SCATTERD(A); % Scatterplot of trainingset
HOLD ON; SCATTERD(R,'ko'); % Add representation set to scatterplot
PLOTC(W); % Plot classifier
datasets, mappings, scalem, klms, proxm, labeld, kernelc,
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