qdc.m

The pattern recognition matlab toolbox

%QDC Quadratic Bayes Normal Classifier (Bayes-Normal-2)
%
%   [W,R,S,M] = QDC(A,R,S,M)
%   W = A*QDC([],R,S)
%
% INPUT
%   A    Dataset
%   R,S	 Regularization parameters, 0 <= R,S <= 1 
%        (optional; default: no regularization, i.e. R,S = 0)
%   M    Dimension of subspace structure in covariance matrix (default: K,
%        all dimensions)
%
% OUTPUT
%   W    Quadratic Bayes Normal Classifier mapping
%   R    Value of regularization parameter R as used 
%   S    Value of regularization parameter S as used
%   M    Value of regularization parameter M as used
%
% DESCRIPTION
% Computation of the quadratic classifier between the classes of the dataset
% A assuming normal densities. R and S (0 <= R,S <= 1) are regularization
% parameters used for finding the covariance matrix by
% 
%   G = (1-R-S)*G + R*diag(diag(G)) + S*mean(diag(G))*eye(size(G,1))
%
% This covariance matrix is then decomposed as G = W*W' + sigma^2 * eye(K),
% where W is a K x M matrix containing the M leading principal components
% and sigma^2 is the mean of the K-M smallest eigenvalues.
%
% 
% 
% The use of soft labels is supported. The classification A*W is computed by
% NORMAL_MAP.
%
% If R, S or M is NaN the regularisation parameter is optimised by REGOPTC.
% The best result are usually obtained by R = 0, S = NaN, M = [], or by
% R = 0, S = 0, M = NaN (which is for problems of moderate or low dimensionality
% faster). If no regularisation is supplied a pseudo-inverse of the
% covariance matrix is used in case it is close to singular.
%
% EXAMPLES
% See PREX_MCPLOT, PREX_PLOTC.
%
% REFERENCES
% 1. R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, 2nd
% edition, John Wiley and Sons, New York, 2001. 
% 2. A. Webb, Statistical Pattern Recognition, John Wiley & Sons, 
% New York, 2002.
%
% SEE ALSO
% MAPPINGS, DATASETS, REGOPTC, NMC, NMSC, LDC, UDC, QUADRC, NORMAL_MAP

% Copyright: R.P.W. Duin, [e-mail]
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands

% $Id: qdc.m,v 1.7 2008/03/20 09:25:10 duin Exp $

function [w,r,s,dim] = qdc(a,r,s,dim)

	prtrace(mfilename);

	if (nargin < 4)
		prwarning(4,'subspace dimensionality M not given, assuming K');
		dim = [];
	end
	if (nargin < 3) | isempty(s)
		prwarning(4,'Regularisation parameter S not given, assuming 0.');
		s = 0; 
	end
	if (nargin < 2) | isempty(r)
		prwarning(4,'Regularisation parameter R not given, assuming 0.');
		r = 0;
	end
	
	if (nargin < 1) | (isempty(a))      % No input arguments: 
		w = mapping(mfilename,{r,s,dim}); % return an untrained mapping.
		
	elseif any(isnan([r,s,dim]))        % optimize regularisation parameters
		defs = {0,0,[]};
		parmin_max = [1e-8,9.9999e-1;1e-8,9.9999e-1;1,size(a,2)];
		[w,r,s,dim] = regoptc(a,mfilename,{r,s,dim},defs,[3 2 1],parmin_max,testc([],'soft'),[1 1 0]);
			
	else % training
		
		islabtype(a,'crisp','soft'); % Assert A has the right labtype.
		isvaldfile(a,2,2); % at least 2 objects per class, 2 classes

		[m,k,c] = getsize(a);

		% If the subspace dimensionality is not given, set it to all dimensions.

		if (isempty(dim)), dim = k; end;
		
		dim = round(dim);
		if (dim < 1) | (dim > k)
			error ('Number of dimensions M should lie in the range [1,K].');
		end

		[U,G] = meancov(a);

		% Calculate means and priors.

		pars.mean  = +U;
		pars.prior = getprior(a);

		% Calculate class covariance matrices.

		pars.cov   = zeros(k,k,c);
		for j = 1:c
			F = G(:,:,j);
		
			% Regularize, if requested.

			if (s > 0) | (r > 0) 
				F = (1-r-s) * F + r * diag(diag(F)) +s*mean(diag(F))*eye(size(F,1));
			end

			% If DIM < K, extract the first DIM principal components and estimate
			% the noise outside the subspace.
			
			if (dim < k)
				dim = min(rank(F)-1,dim);
				[eigvec,eigval] = eig(F); eigval = diag(eigval);
				[dummy,ind] = sort(-eigval);

				% Estimate sigma^2 as avg. eigenvalue outside subspace.
				sigma2 = mean(eigval(ind(dim+1:end)));

				% Subspace basis: first DIM eigenvectors * sqrt(eigenvalues).
				F = eigvec(:,ind(1:dim)) * diag(eigval(ind(1:dim))) * eigvec(:,ind(1:dim))' + ...
				    sigma2 * eye(k);
			end

			pars.cov(:,:,j) = F;
		end

		w = normal_map(pars,getlab(U),k,c);
		w = setcost(w,a);
		
	end

	w = setname(w,'Bayes-Normal-2');

return;