% TRAIN_KPCA_DENOIS Training of kernel PCA model for image denoising. %% Description:%  The kernel PCA model is trained to describe an input%  class of images corrupted by noise. The training data %  contains images corrupted by noise and corresponding %  ground truth. The free paramaters of the kernel PCA%  are tuned by cross-validation. The objective function %  is a sum of squared differences between ground truth %  images and reconstructed images. The greedy KPCA algorithm %  is used to train the kernel PCA model.%% See also%  GREEDYKPCA, KPCAREC, KPCA.%% About: Statistical Pattern Recognition Toolbox% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac% <a href="http://www.cvut.cz">Czech Technical University Prague</a>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a>% Modifications:% 07-jun-2004, VF% 06-jun-2004, VF% 17-mar-2004, VF% Setting% -------------------------------------options.ker = 'rbf';   % kerneloptions.m = 500;       % # of basis vectorsoptions.p = 10;        % deth of search for the best basis vectoroptions.verb = 1;% # folds for cross-validation; % num_folds = 1 means 50/50 - training/testing partnum_folds = 1;  % algorithm to compute kernel PCA%KPCA_Algo = 'kpca';KPCA_Algo = 'greedykpca';% parameters to be evaluated by cross-validation:%New_Dim_Range = [50 100 200 300]; % usps%Arg_Range = [3.5 4 5 6 7 8];      % usps%New_Dim_Range = [1 2];   % noisy_circle%Arg_Range = [0.5 1 2 3]; % noisy_circle% input/output filesinput_data_file = 'noisy_circle';output_data_file = [];%input_data_file = '/home.dokt/xfrancv/data/usps/usps_noisy';%output_data_file = 'USPSModelGreedyKPCA';% Loads training and testing data.% -------------------------------------load(input_data_file,'trn','tst');[dim,num_data] = size(trn.X);% Data partitioning for cross-validation[itrn,itst] = crossval(num_data,num_folds);% Tuning kernel PCA model% -------------------------------------Mse = [];for arg = Arg_Range,  for new_dim = New_Dim_Range,        fprintf('\nnew_dim = %d, arg = %f\n', new_dim, arg);       cv_mse = 0;      for i=1:num_folds,          fprintf('\n');      % training and validation part of data      trn_X = trn.gnd_X(:,itrn{i});      val_gnd_X = trn.gnd_X(:,itst{i});      val_corr_X = trn.X(:,itst{i});          fprintf('Computing Kernel PCA...');      options.arg = arg;      options.new_dim = new_dim;      kpca_model = feval( KPCA_Algo, trn_X, options);      fprintf('done.\n');      % data restoration      val_reconst_X = kpcarec(val_corr_X, kpca_model);        % compute error      dummy = (val_reconst_X - val_gnd_X).^2;      mse = sum(dummy(:))/size(val_gnd_X,2);        fprintf('folder %d/%d: validation errors mse=%f\n', ...        i, num_folds, mse);         cv_mse = cv_mse + mse;    end    % compute cross-validation error    cv_mse = cv_mse/num_folds;     Mse(find(new_dim==New_Dim_Range),find(arg==Arg_Range)) = cv_mse;     fprintf('Kernel arg = %f: mse = %f\n', arg, cv_mse);  endend% take the best parameters%----------------------------------------------[inx1,inx2] = find(Mse==min(Mse(:)));fprintf('\nMin(mse) = %f, dim = %f, arg = %f\n', ...   Mse(inx1,inx2), New_Dim_Range(inx1), Arg_Range(inx2) );% compute kernel PCA model with best parameters% using all training data%---------------------------------------------fprintf('Computing optimal Kernel PCA...');options.arg = Arg_Range(inx2);options.new_dim = New_Dim_Range(inx1);kpca_model = feval( KPCA_Algo, trn.X, options);fprintf('done.\n');if isempty(output_data_file),  % plot results of tuning  figure; hold on;  xlabel('\sigma'); ylabel('mse');  h = [];  clear Str;  for i=1:length(New_Dim_Range),    h = [h, plot(Arg_Range, Mse(i,:),marker_color(i) )];    Str{i} = sprintf('dim = %d', New_Dim_Range(i));  end  legend(h,Str);else  % save model to file  save(output_data_file,'Arg_Range','New_Dim_Range',...     'options','Mse','num_folds','input_data_file',...     'output_data_file','KPCA_Algo','kpca_model');end% plot denosing in 2D case only%-------------------------------------if dim == 2 & isempty(output_data_file),  X = kpcarec(tst.X,kpca_model);  mse = sum(sum((X-tst.gnd_X).^2 ));  fprintf('\ntest mse=%f\n', mse);  figure; hold on;  h0=ppatterns(tst.gnd_X,'r+');  h1=ppatterns(tst.X,'gx');  h2=ppatterns(X,'bo');  legend([h0 h1 h2],'Ground truth','Noisy','Reconst');end% EOF