dslvq.m

模式识别matlab工具箱,包括SVM,ICA,PCA,NN等等模式识别算法,很有参考价值

function [features, targets, w] = DSLVQ(train_features, train_targets, Nmu, region, plot_on)

%Reduce the number of data points using distinction sensitive linear vector quantization 
%Inputs:
%	train_features	- Input features
%	train_targets	- Input targets
%	Nmu				- Number of output data points
%	region			- Decision region vector (ununused)
%   plot_on         - Plot stages of the algorithm
%
%Outputs
%	features			- New features
%	targets			- New targets
%	w					- Weights vector

if (nargin < 5),
    plot_on = 0;
end

Ndim  = size(train_features, 1);
alpha = 0.9;
beta	= 0.1;
L		= length(train_targets);
dist	= zeros(Nmu,L);
label = zeros(1,L);

%Initialize the mu's
mu			= randn(Ndim,Nmu);
mu			= sqrtm(cov(train_features',1))*mu + mean(train_features')'*ones(1,Nmu);
mu_target= rand(1,Nmu)>.5;
old_mu	= zeros(Ndim,Nmu);

%Initialize the weight vector
w			= ones(size(train_features,1),1);

while (sum(sum(abs(mu - old_mu))) > 0.1),
   old_mu = mu;
   
   %Classify all the features to one of the mu's
   for i = 1:Nmu,
      dist(i,:) = sum(((w*ones(1,L)).*(train_features - mu(:,i)*ones(1,L))).^2);      
   end
      
   %For each sample, ...
   for i = 1:L,
      %Find the nearest neighbor classified correctly, and the nearest one classified
      %incorrectly
      d	= dist(:,i).*(mu_target'-.5)*2;
      dp = d;dn = d;
      dp(find(dp<0)) = nan;
      dn(find(dn>0)) = nan;
      ci = find(dp == min(dp));
      cj = find(dn == max(dn));
      if (isempty(ci) | isempty(cj)),
         break
      end
      di = abs(train_features(:,i) - mu(:,ci));
  	   dj = abs(train_features(:,i) - mu(:,cj));
      wn = (di-dj)/sum(abs(di-dj));
  	   nw	= w + beta*(wn - w);
     	nw(find(nw>1)) 	= 1;
      nw(find(nw<1e-4)) = 1e-4;      
      w	= nw./sum(abs(nw));
   end
      
   %Label the points
   [m,label] = min(dist);

   %Label the mu's
	for i = 1:Nmu,
   	if (length(train_targets(:,find(label == i))) > 0),
      	mu_target(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5);
	   end
	end	
   
   %Recompute the mu's
   for i = 1:Nmu,
      indices = find(label == i);
      if ~isempty(indices),
         Q		  = ones(Ndim,1) * (2*(train_targets(indices) == mu_target(i)) - 1);
         mu(:,i) = mu(:,i) + mean(((train_features(:,indices)-mu(:,i)*ones(1,length(indices))).*Q)')'*alpha;
      end
      
   end
   
   alpha = 0.95 * alpha;
   beta	= 0.95 * beta;
   
   if (plot_on == 1),
       plot_process(mu)
   end

end

%Make the decision region
targets = zeros(1,Nmu);
if (Nmu > 1),
	for i = 1:Nmu,
   	if (length(train_targets(:,find(label == i))) > 0),
      	targets(i) = (sum(train_targets(:,find(label == i)))/length(train_targets(:,find(label == i))) > .5);
   	end
	end
else
   %There is only one center
   targets = (sum(train_targets)/length(train_targets) > .5);
end

features = mu;