ITE / code / H_I_D / base_estimators / IKCCA_estimation.m

function [I] = IKCCA_estimation(Y,ds,co)
%Estimates mutual information (I) using the KCCA (kernel canonical correlation analysis) method. 
%
%INPUT:
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: initialized mutual information estimator object.
%REFERENCE:
%   Zoltan Szabo, Barnabas Poczos, Andras Lorincz: Undercomplete Blind Subspace Deconvolution. Journal of Machine Learning Research 8(May):1063-1095, 2007. (multidimensional case, i.e., ds(j)>=1)
%   Francis Bach, Michael I. Jordan. Kernel Independent Component Analysis. Journal of Machine Learning Research, 3: 1-48, 2002. (one-dimensional case, i.e., ds(1)=ds(2)=...=ds(end)=1)
%
%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
%
%This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox.
%
%ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by
%the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
%
%This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
%
%You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.

%co.mult:OK.

R = compute_matrixR_KCCA_KGV(Y,ds,co);

[temp,eig_min] = eigs(R,1,'SM');%compute the smallest eigenvalue of R
I = -log(eig_min) / 2;
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