pcaanalysis.m

It is for Face Recognition

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function pcaAnalysis()
%   z.li, 12-16-2003.
%   covariance matrix eigen analysis
% function dependency:
%   - n/a
% input:
%   y - d x n data
%   K - desired dimension, K < n
% output:
%   A - d x d eig matrix
%   lat - eig vals
%   mx - mean 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%function [A, lat, mx]=pcaAnalysis(y, K)
function [A, lat, mx]=pcaAnalysis(y, K)
 dbg='n';
 if dbg=='y'
   clear;
   y = load('fa-icons-w12h16-3d.dat');
   K = 6;
 end

 x = y';            % make it n x d matrix
 [n d] = size(x);
 mx = mean(x);      % row vec
 for k=1:n          % remove mean
    x(k,:) = x(k,:) - mx;
 end

 [pc, s, lat, ts]=princomp(x);

 A = [];
 for j=1:K
    A(:,j) = pc(:,j);
 end
 A = A';

 noplot = 'n';
 if noplot == 'n'
   hold on;
   subplot(2,2,1); plot(A(1,:)); xlabel('\fontsize{12} 1st basis');
   subplot(2,2,2); plot(A(2,:)); xlabel('\fontsize{12} 2nd basis');
   subplot(2,2,3); stem(lat, '.'); xlabel('\fontsize{12} eigen values');
   subplot(2,2,4); hold on; xlabel('\fontsize{12} distrb in sub space');
   for k=1:n
      pct = A*y(:,k);
      plot(pct(1), pct(2), '.');
   end
   
 end