Is this the right way to obtain first PC?

I have used the following standard code for PCA.

% code
%function [signals,PC,V] = pca1(data) 
% PCA1: Perform PCA using covariance. 
% data - MxN matrix of input data 
% (M dimensions, N trials) 
% signals - MxN matrix of projected data 
% PC - each column is a PC 
% V - Mx1 matrix of variances 
[M,N] = size(data); 
% subtract off the mean for each dimension 
mn = mean(data,2); 
data = data - repmat(mn,1,N); 
% calculate the covariance matrix 
covariance = 1 / (N-1) * data * data'; 
% find the eigenvectors and eigenvalues 
[PC, V] = eig(covariance); 
% extract diagonal of matrix as vector 
V = diag(V); 
% sort the variances in decreasing order 
[junk, rindices] = sort(-1*V); 
V = V(rindices); PC = PC(:,rindices); 
% project the original data set 
signals = PC' * data; code
end

I now want to extract only the first principal component. The code which calls the function and (hopefully) obtains the first principal component is:

    % clc,clear ;
I=imread('lena.jpg');
I1=im2double(I);
[signals,pc,V]=pca1(I1);
disp('signals - MxN matrix of projected data ');
disp(signals);
subplot(2,1,1);
plot(signals);
disp('PC - each column is a PC');
disp(pc);
subplot(2,1,2);
plot(pc);
fpc=pc(1);
disp('First principal component');
disp(fpc)
disp('V - Mx1 matrix of variances');
disp(V);

Does 'fpc' return the first principal component? Is what i have done ok?

Answer 1

[sidra]

Is this the right way?

[Neo]

What does V = V(rindices); PC = PC(:,rindices); mean?

[Walter Roberson]

rindices was returned by the call to sort() and is the indices of the values in descending order. V(rindices) is V re-ordered into that descending order, and PC(:,rindices) is PC with the columns reordered in that same order.