joint_diag_r.m

用于盲信号分离的独立分量分析ICA算法

function [ V ,  qDs ]= rjd(A,threshold)
%***************************************
% joint diagonalization (possibly
% approximate) of REAL matrices.
%***************************************
% This function minimizes a joint diagonality criterion
% through n matrices of size m by m.
%
% Input :
% * the  n by nm matrix A is the concatenation of m matrices
%   with size n by n. We denote A = [ A1 A2 .... An ]
% * threshold is an optional small number (typically = 1.0e-8 see below).
%
% Output :
% * V is a n by n orthogonal matrix.
% * qDs is the concatenation of (quasi)diagonal n by n matrices:
%   qDs = [ D1 D2 ... Dn ] where A1 = V*D1*V' ,..., An =V*Dn*V'.
%
% The algorithm finds an orthogonal matrix V
% such that the matrices D1,...,Dn  are as diagonal as possible,
% providing a kind of `average eigen-structure' shared
% by the matrices A1 ,..., An.
% If the matrices A1,...,An do have an exact common eigen-structure
% ie a common othonormal set eigenvectors, then the algorithm finds it.
% The eigenvectors THEN are the column vectors of V
% and D1, ...,Dn are diagonal matrices.
% 
% The algorithm implements a properly extended Jacobi algorithm.
% The algorithm stops when all the Givens rotations in a sweep
% have sines smaller than 'threshold'.
% In many applications, the notion of approximate joint diagonalization
% is ad hoc and very small values of threshold do not make sense
% because the diagonality criterion itself is ad hoc.
% Hence, it is often not necessary to push the accuracy of
% the rotation matrix V to the machine precision.
% It is defaulted here to the square root of the machine precision.
% 
%
% Author : Jean-Francois Cardoso. [e-mail]
% This software is for non commercial use only.
% It is freeware but not in the public domain.
% A version for the complex case is available
% upon request at [e-mail]
%-----------------------------------------------------
% Two References:
%
% The algorithm is explained in:
%
%@article{SC-siam,
%   HTML =	"ftp://sig.enst.fr/pub/jfc/Papers/siam_note.ps.gz",
%   author = "Jean-Fran\c{c}ois Cardoso and Antoine Souloumiac",
%   journal = "{SIAM} J. Mat. Anal. Appl.",
%   title = "Jacobi angles for simultaneous diagonalization",
%   pages = "161--164",
%   volume = "17",
%   number = "1",
%   month = jan,
%   year = {1995}}
%
%  The perturbation analysis is described in
%
%@techreport{PertDJ,
%   author = "{J.F. Cardoso}",
%   HTML =	"ftp://sig.enst.fr/pub/jfc/Papers/joint_diag_pert_an.ps",
%   institution = "T\'{e}l\'{e}com {P}aris",
%   title = "Perturbation of joint diagonalizers. Ref\# 94D027",
%   year = "1994" }
%
%
%
[m,nm] = size(A);
V=eye(m);

if nargin==1, threshold=sqrt(eps); end;

encore=1;
while encore, encore=0;
 for p=1:m-1,
  for q=p+1:m,
   %%%computation of Givens rotations
   g=[ A(p,p:m:nm)-A(q,q:m:nm) ; A(p,q:m:nm)+A(q,p:m:nm) ];
   g=g*g';
   ton =g(1,1)-g(2,2); toff=g(1,2)+g(2,1);
   theta=0.5*atan2( toff , ton+sqrt(ton*ton+toff*toff) );
   c=cos(theta);s=sin(theta);
   encore=encore | (abs(s)>threshold);
    %%%update of the A and V matrices 
   if (abs(s)>threshold) ,
    Mp=A(:,p:m:nm);Mq=A(:,q:m:nm);
    A(:,p:m:nm)=c*Mp+s*Mq;A(:,q:m:nm)=c*Mq-s*Mp;
    rowp=A(p,:);rowq=A(q,:);
    A(p,:)=c*rowp+s*rowq;A(q,:)=c*rowq-s*rowp;
    temp=V(:,p);V(:,p)=c*V(:,p)+s*V(:,q); V(:,q)=c*V(:,q)-s*temp;
   end%%of the if
  end%%of the loop on q
 end%%of the loop on p
end%%of the while loop
qDs = A ;