%MDSC Trainable classifier for Manhatten Dissimilarity Space
%
% W = MDSC(A,R,CLASSF)
% W = A*MDSC([],R,CLASSF)
% W = A*MDSC(R,CLASSF)
% W = A*MDSC(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.
% CLASSF Classifier used in dissimilarity space
% Default LIBSVC([],[],100)
% X Test set.
%
% OUTPUT
% W Resulting, trained feature space classifier
% D Classification matrix
%
% DESCRIPTION
% This is a dissimilarity based classifier intended for a feature
% respresentation. The training set A is used to compute for every class
% its own eigenspace. All eigenvectors are used. A dissimilarity space is
% built by the Manhatten (L1, or Minkowsky-1 or city block) distances
% between training objects A or test objects X and the representation
% objects R after transformation (i.e. rotation) to the eigenspace of
% the class of the particular represention object.
%
% Note that Euclidean distances are not affected by rotation, but Manhatten
% distances are.
%
% New objects in feature space can be classified by D = X*W or by
% D = PRMAP(X,W). Labels can be found by LAB = D*LABELD or LAB = LABELD(D).
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, MAPPINGS, FDSC
% Copyright: S.W. Kim, 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 = mdsc(varargin)
mapname = 'ManhattenDisSpaceC';
argin = shiftargin(varargin,nargin==1);
argin = shiftargin(argin,'prmapping',2);
argin = setdefaults(argin,[],[],libsvc([],[],100));
if mapping_task(argin,'definition')
w = define_mapping(argin,'untrained',mapname);
elseif mapping_task(argin,'training') % Train a mapping.
[a,r,classf] = deal(argin{:});
isvaldfile(a,2,2); % at least 2 objects per class, 2 classes
if isempty(r)
r = a; % default representation set is the training set
elseif is_scalar(r)
[r,a] = gendat(a,r); % or we take a random fraction
end
% Training set and representation set should belong to the same set of
% classes. Let us check that.
laba = getlablist(a);
labr = getlablist(r);
[nlab1,nlab2,lablist] = renumlab(laba,labr);
c = size(lablist,1);
if any([length(unique(nlab1)) length(unique(nlab2))] ~= c)
error('Training set and representation set should contain the same classes')
end
% We are now ready to compute the classifier. The set of class dependent
% rotations will be stored in a stacked set of mappings v.
v = cell(1,c);
for j=1:c % compute the mapping for every class
b = seldat(a,getclassi(a,lablist(j,:))); % training set of class j
[e,d] = preig(covm(b)); % its eigenvalue decomposition
u = affine(e); % the rotation, apply it to the represention
s = seldat(r,getclassi(r,lablist(j,:)))*u; % objects of the same class
v{j} = u*proxm(s,'m',1); % store the rotation and the proximity mapping
% to the rotated representation objects
end
v = stacked(v); % combine all mappings as a stacked mapping
d = a*v; % compute dissimilarity matrix for the training set
n = disnorm(d); % find a proper normalisation and ...
w = a*(v*n*classf); % apply it to the training set, compute the classifier
% and include the mapping for use by the test objects
w = setname(w,mapname);
end
return