function [co] = IGV_initialization(mult) %Initialization of the GV (generalized variance) estimator. % %Note: % 1)The estimator is treated as a cost object (co). % 2)We make use of the naming convention 'I<name>_initialization', to ease embedding new mutual information estimation methods. % 3)For GV, the corresponding 'IGV_estimation.m' procedure has not been implemented, since GV is used in case of cost_type = 'Ipairwise1d', where the similarity matrix can be computed more % efficiently for GV (see 'I_similarity_matrix.m'). % %INPUT: % mult: is a multiplicative constant relevant (needed) in the estimation; '=1' means yes, '=0' no. %OUTPUT: % co: object (structure). %REFERENCE: % Zoltan Szabo, Andras Lorincz: Real and Complex Independent Subspace Analysis by Generalized Variance. ICARN-2006, pages 85-88. % %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/>. %mandatory fields: co.name = 'GV'; co.mult = mult; %other fields: %xor: co.dependency = 'corr';%use nonlinear correlations instead of nonlinear covariances. %co.dependency = 'cov';%use nonlinear covariances instead of nonlinear correlations.