1. Zoltán Szabó
  2. ITE


ITE / code / H_I_D_C / base_estimators / IQMI_ED_KDE_iChol_estimation.m

function [I] = IQMI_ED_KDE_iChol_estimation(Y,ds,co)
%Estimates the Euclidean distance based quadratic mutual information (I) approximately, applying Gaussian KDE (kernel density estimation) and incomplete Cholesky decomposition. 
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: initialized mutual information estimator object.
%      Sohan Seth and Jose C. Principe. On speeding up computation in information theoretic learning. In International Joint Conference on Neural Networks (IJCNN), pages 2883-2887, 2009.
%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/>.


    if length(ds)~=2 %M=2
        error('The number of components must be 2 for this estimator.');

I = qmi_ed(Y(1:ds(1),:).',Y(ds(1)+1:ds(1)+ds(2),:).',co.sigma);