www.pudn.com > icaML.zip > icaML_bic.m

function [P,logP]=icaML_bic(X,K,draw)   
% icaML_bic : ICA by ML with square mixing matrix and no noise. 
% 
% function [P,logP]=icaML_bic(X,[K],[draw])     Use Bayes Information Criterion (BIC) [1] to 
%                                               estimate number of components with the maximum 
%                                               likelihood ICA (icaML) algorithm. SVD is used for  
%                                               dimension reduction. 
%                                        
%                                               X     : Mixed signal matrix [M x N] or a struct 
%                                                       holding the solution to the [U,S,V]=SVD(X),  
%                                                       thus X.U=U, X.S=S and X.V=V. 
%                                               K     : Vector or scalar holding the number of  
%                                                       IC components to investigate. Values 
%                                                       must be greater than 1. Default K=[2:10]. 
%                                               draw  : Output run-time information if draw=1.  
%                                                       Default draw=0.             
% 
%                                               P     : Normalized model probability. 
%                                               logP  : Model negative log probability. 
%                                        
% - by Thomas Kolenda 2002 - IMM, Technical University of Denmark 
% - version 1.2a 
 
% Bibtex references: 
% [1]   
%  @article{Hansen2001BIC, 
%       author =       "Hansen, L. K. and Larsen, J. and Kolenda, T.", 
%       title =        "Blind Detection of Independent Dynamic Components", 
%       journal =      "In proc. IEEE ICASSP'2001", 
%       volume =       "5", 
%       pages =        "3197--3200", 
%       year =         "2001",   
%       url =          "http://www.imm.dtu.dk/pubdb/views/edoc_download.php/827/pdf/imm827.pdf",  
%  } 
 
 
% parse input 
if nargin<2, 
  K = [2:10]; 
end 
if nargin<3, 
  draw = 0; 
end 
 
if isstruct(X)==0, 
  % Reduce dimension with PCA 
  [M,N] = size(X); 
  if N>M % Transpose if matrix is flat to speed up the svd and later transpose back again 
    if draw==1,disp('Transpose SVD');end 
    [V,D,U] = svd(X',0); 
  else;   
    if draw==1,disp('SVD');end 
    [U,D,V] = svd(X,0); 
  end; 
else 
  U = X.U; 
  D = X.S; 
  V = X.V; 
end; 
 
M = size(U,1); 
N = size(V,1); 
X = D*V'; 
 
if draw==1,disp(sprintf('M=%d N=%d',M,N));end 
 
index=0;   
for k = K, 
  index = index+1; 
  if draw==1,disp(sprintf('dim=%d',k));end 
 
  % estimate ICA 
  [S,A] = icaML(X(1:k,:)); 
 
  % log likelihood of ica 
  logP(index) = N*log(abs(det(inv(A)))) - sum(sum(log( cosh(S) ))) - N*k*log(pi); 
 
  % log likelihood of non included PCA space 
  if k