gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\stprtool\pca\pcademo1.m
% PCADEMO1 demo on use of standard PCA. % Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac % (c) Czech Technical University Prague, http://cmp.felk.cvut.cz % Modifications: % 5-July-2001, V.Franc echo on; % First, we load data from file and display them. Use 'creatset' to % interactively create your own data. pause; % press anykey data=load('pcaexam1'); ppoints(data.X, data.I); % Now, we compute orthonormal projection matrix and project the data. pause; % press anykey [T, sumErr, eigv ] = spca( data.X, 2 ); Y=T*data.X; % projection to a new space % The second coordinate will be replaced by constant and then the data % will be projected back to the original space (reconstruction). pause; % press anykey Y(2,:) = repmat( mean(Y(2,:)), 1 , sum(data.K)); Xrec = T' * Y; % Finaly, we display recostructed data and the conections to the original ones. pause; % press anykey hold on; ppoints(Xrec, 2*data.I ); % display reconstructed data with another label. for i = 1:sum(data.K), origP = data.X(:,i); recP = Xrec(:,i); diff(i) = norm( origP-recP )^2; % square reconstruction error plot([origP(1),recP(1)],[origP(2),recP(2)],'k'); % conection echo off; end echo on; mean(diff) % mean value of squared lenghts of connection black lines eigv(2) % eigenvalue of the neglected eigenvector echo off;