Netlab

Ian Nabney (view profile)

• 1 file
• 3.06667

06 Nov 2002 (Updated )

Pattern analysis toolbox.

eigdec(x, N)
function [evals, evec] = eigdec(x, N)
%EIGDEC	Sorted eigendecomposition
%
%	Description
%	 EVALS = EIGDEC(X, N computes the largest N eigenvalues of the
%	matrix X in descending order.  [EVALS, EVEC] = EIGDEC(X, N) also
%	computes the corresponding eigenvectors.
%
%	PCA, PPCA
%

%	Copyright (c) Ian T Nabney (1996-2001)

if nargout == 1
evals_only = logical(1);
else
evals_only = logical(0);
end

if N ~= round(N) | N < 1 | N > size(x, 2)
error('Number of PCs must be integer, >0, < dim');
end

% Find the eigenvalues of the data covariance matrix
if evals_only
% Use eig function as always more efficient than eigs here
temp_evals = eig(x);
else
% Use eig function unless fraction of eigenvalues required is tiny
if (N/size(x, 2)) > 0.04
[temp_evec, temp_evals] = eig(x);
else
options.disp = 0;
[temp_evec, temp_evals] = eigs(x, N, 'LM', options);
end
temp_evals = diag(temp_evals);
end

% Eigenvalues nearly always returned in descending order, but just
% to make sure.....
[evals perm] = sort(-temp_evals);
evals = -evals(1:N);
if ~evals_only
if evals == temp_evals(1:N)
% Originals were in order
evec = temp_evec(:, 1:N);
return
else
% Need to reorder the eigenvectors
for i=1:N
evec(:,i) = temp_evec(:,perm(i));
end
end
end