%FDSC Trainable Feature based Dissimilarity Space Classifier
%
% 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
%
% DESCRIPTION
% 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 = PRMAP(B,W). Labels can be found by LAB = D*LABELD or LAB = LABELD(D).
%
% EXAMPLE
% 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
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, MAPPINGS, SCALEM, KLMS, PROXM, LABELD, KERNELC
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function w = fdsc(varargin)
argin = setdefaults(varargin,[],[],[],'distance',1,libsvc([],[],100));
[a,r,featmap,type,p,classf] = deal(argin{:});
if mapping_task(argin,'definition')
w1 = prmapping(mfilename,'untrained',{r,featmap,type,p,classf});
w = setname(w1,['DisSpace-',getname(classf)]);
return
end
isvaldfile(a,1,2); % at least 1 object per class, 2 classes
if isdataset(a) | isdatafile(a)
isvaldfile(a,1,1);
if isempty(featmap)
v = scalem(a); % shift mean to the origin
elseif isuntrained(featmap)
v = a*featmap;
elseif ismapping(featmap)
v = featmap;
else
error('Feature mapping expected')
end
if isempty(r)
r = a;
elseif is_scalar(r)
[r,a] = gendat(a,r);
end
w = proxm(r*v,type,p);
if ~isuntrained(classf)
error('Untrained classifier expected')
end
b = a*v*w;
%b = testdatasize(b);
n = disnorm(b);
u = b*n*classf;
w = v*w*n*u;
else
error('Unexpected input')
end