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+5 0CHANGELOG.txt

+4 4README.md

+3 3code/H_I_D_A_C/A_estimation.m

+2 2code/H_I_D_A_C/A_initialization.m

+2 2code/H_I_D_A_C/C_estimation.m

+1 1code/H_I_D_A_C/C_initialization.m

+37 0code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_Lapl_estimation.m

+30 0code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_Lapl_initialization.m

+37 0code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_iChol_estimation.m

+30 0code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_iChol_initialization.m

+36 0code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_direct_estimation.m

+31 0code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_direct_initialization.m

+37 0code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_iChol_estimation.m

+31 0code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_iChol_initialization.m

+36 0code/H_I_D_A_C/base_estimators/ACorrEntr_KDE_direct_estimation.m

+30 0code/H_I_D_A_C/base_estimators/ACorrEntr_KDE_direct_initialization.m

+2 2code/H_I_D_A_C/base_estimators/ASpearman1_estimation.m

+1 1code/H_I_D_A_C/base_estimators/ASpearman1_initialization.m

+2 2code/H_I_D_A_C/base_estimators/ASpearman2_estimation.m

+1 1code/H_I_D_A_C/base_estimators/ASpearman2_initialization.m

+2 2code/H_I_D_A_C/base_estimators/ASpearman3_estimation.m

+1 1code/H_I_D_A_C/base_estimators/ASpearman3_initialization.m

+2 2code/H_I_D_A_C/base_estimators/CCE_kNN_k_estimation.m

+1 1code/H_I_D_A_C/base_estimators/CCE_kNN_k_initialization.m

+44 0code/H_I_D_A_C/meta_estimators/ACCIM_estimation.m

+31 0code/H_I_D_A_C/meta_estimators/ACCIM_initialization.m

+44 0code/H_I_D_A_C/meta_estimators/ACIM_estimation.m

+31 0code/H_I_D_A_C/meta_estimators/ACIM_initialization.m

+1 1code/H_I_D_A_C/meta_estimators/DEnergyDist_DMMD_estimation.m

+41 0code/H_I_D_A_C/meta_estimators/IApprCorrEntr_estimation.m

+33 0code/H_I_D_A_C/meta_estimators/IApprCorrEntr_initialization.m

+1 1code/H_I_D_A_C/meta_estimators/IdCov_IHSIC_estimation.m

+1 0code/IPA/demos/estimate_ISA.m

+37 0code/shared/embedded/ITL/centcorren.m

+32 0code/shared/embedded/ITL/centcorrenexp.m

+48 0code/shared/embedded/ITL/cipexp.m

+33 0code/shared/embedded/ITL/corren.m

+44 0code/shared/embedded/ITL/correncoef.m

+27 0code/shared/embedded/ITL/correncoef_2.m
CHANGELOG.txt
+Approximate correntropy independence measure estimator: added; see 'IApprCorrEntr_initialization.m', 'IApprCorrEntr_estimation.m'.
+Correntropy induced metric, centered correntropy induced metric estimators: added; see 'ACIM_initialization.m', 'ACIM_estimation.m', 'ACCIM_initialization.m', 'ACCIM_estimation.m'.
+Correntropy, centered correntropy, correntropy coefficient estimators: added; see 'ACorrEntr_KDE_direct_initialization.m', 'ACorrEntr_KDE_direct_estimation.m', 'ACCorrEntr_KDE_iChol_initialization.m', 'ACCorrEntr_KDE_iChol_estimation.m', 'ACCorrEntr_KDE_Lapl_initialization.m', 'ACCorrEntr_KDE_Lapl_estimation.m', 'ACorrEntrCoeff_KDE_direct_initialization.m', 'ACorrEntrCoeff_KDE_direct_estimation.m', 'ACorrEntrCoeff_KDE_iChol_initialization.m', 'ACorrEntrCoeff_KDE_iChol_estimation.m'.
Handling of identically constant random variables in distance correlation computation: included; see 'IdCor_estimation.m'.
README.md
 `entropy (H)`: Shannon entropy, R�nyi entropy, Tsallis entropy (Havrda and Charv�t entropy), complex entropy,
 `mutual information (I)`: generalized variance, kernel canonical correlation analysis, kernel generalized variance, HilbertSchmidt independence criterion, Shannon mutual information, L2 mutual information, R�nyi mutual information, Tsallis mutual information, copulabased kernel dependency, multivariate version of Hoeffding's Phi, SchweizerWolff's sigma and kappa, complex mutual information, CauchySchwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation,
+ `mutual information (I)`: generalized variance, kernel canonical correlation analysis, kernel generalized variance, HilbertSchmidt independence criterion, Shannon mutual information, L2 mutual information, R�nyi mutual information, Tsallis mutual information, copulabased kernel dependency, multivariate version of Hoeffding's Phi, SchweizerWolff's sigma and kappa, complex mutual information, CauchySchwartz quadratic mutual information, Euclidean distance based quadratic mutual information, distance covariance, distance correlation, approximate correntropy independence measure,
 `divergence (D)`: KullbackLeibler divergence (relative entropy), L2 divergence, R�nyi divergence, Tsallis divergence, Hellinger distance, Bhattacharyya distance, maximum mean discrepancy (kernel distance, an integral probability metric), Jdistance (symmetrised KullbackLeibler divergence), CauchySchwartz divergence, Euclidean distance based divergence, energy distance (specially the CramerVon Mises distance),
 `association measures (A)`, including `measures of concordance`: multivariate extensions of Spearman's rho (Spearman's rank correlation coefficient, grade correlation coefficient),
+ `association measures (A)`, including `measures of concordance`: multivariate extensions of Spearman's rho (Spearman's rank correlation coefficient, grade correlation coefficient), correntropy, centered correntropy, correntropy coefficient, correntropy induced metric, centered correntropy induced metric,
 code: [zip](https://bitbucket.org/szzoli/ite/downloads/ITE0.26_code.zip), [tar.bz2](https://bitbucket.org/szzoli/ite/downloads/ITE0.26_code.tar.bz2),
+ code: [zip](https://bitbucket.org/szzoli/ite/downloads/ITE0.27_code.zip), [tar.bz2](https://bitbucket.org/szzoli/ite/downloads/ITE0.27_code.tar.bz2),
code/H_I_D_A_C/A_estimation.m
%Estimates association A(y^1,...,y^M), where the m^th subspace is ds(m)dimensional; using the method/cost object co.
+%Estimates association measure A(y^1,...,y^M), where the m^th subspace is ds(m)dimensional; using the method/cost object co.
%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
code/H_I_D_A_C/A_initialization.m
+%Initialization of an A (association measure) estimator. The estimator is treated as a cost object (co).
code/H_I_D_A_C/C_estimation.m
%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
code/H_I_D_A_C/C_initialization.m
code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_Lapl_estimation.m
+%Estimates the centered correntropy (A) using Laplacian KDE (kernel density estimation) + a sorting based trick.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+% Aiyou Chen. Fast kernel density independent component analysis. In Independent Component Analysis and Blind Signal Separation (ICA), pages 2431, 2006. (sorting based trick)
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_Lapl_initialization.m
+%Initialization of the Laplacian KDE (kernel density estimation) + a sorting trick based centered correntropy estimator.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_iChol_estimation.m
+%Estimates the centered correntropy (A) using Gaussian KDE (kernel density estimation) + incomplete Cholesky decomposition.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+% Sohan Seth and Jose C. Principe. On speeding up computation in information theoretic learning. In International Joint Conference on Neural Networks (IJCNN), pages 28832887, 2009.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACCorrEntr_KDE_iChol_initialization.m
+%Initialization of the Gaussian KDE (kernel density estimation) + incomplete Cholesky decomposition based centered correntropy estimator.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_direct_estimation.m
+%Estimates the correntropy coefficient (A) directly using Gaussian KDE (kernel density estimation).
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_direct_initialization.m
+%Initialization of the Gaussian KDE (kernel density estimation) based direct correntropy coefficient estimator.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_iChol_estimation.m
+%Estimates the correntropy coefficient (A) using Gaussian KDE (kernel density estimation) + incomplete Cholesky decomposition.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+% Sohan Seth and Jose C. Principe. On speeding up computation in information theoretic learning. In International Joint Conference on Neural Networks (IJCNN), pages 28832887, 2009.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACorrEntrCoeff_KDE_iChol_initialization.m
+%Initialization of the Gaussian KDE (kernel density estimation) + incomplete Cholesky decomposition based correntropy coefficient estimator.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACorrEntr_KDE_direct_estimation.m
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ACorrEntr_KDE_direct_initialization.m
+%Initialization of the Gaussian KDE (kernel density estimation) based direct correntropy estimator.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/base_estimators/ASpearman1_estimation.m
%We use the naming convention 'A<name>_estimation' to ease embedding new association estimator methods.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
% Friedrich Shmid, Rafael Schmidt, Thomas Blumentritt, Sandra Gaiser, and Martin Ruppert. Copula Theory and Its Applications, Chapter Copula based Measures of Multivariate Association. Lecture Notes in Statistics. Springer, 2010.
code/H_I_D_A_C/base_estimators/ASpearman1_initialization.m
% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association estimator methods.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
code/H_I_D_A_C/base_estimators/ASpearman2_estimation.m
%We use the naming convention 'A<name>_estimation' to ease embedding new association estimator methods.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
% Friedrich Shmid, Rafael Schmidt, Thomas Blumentritt, Sandra Gaiser, and Martin Ruppert. Copula Theory and Its Applications, Chapter Copula based Measures of Multivariate Association. Lecture Notes in Statistics. Springer, 2010.
code/H_I_D_A_C/base_estimators/ASpearman2_initialization.m
% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association estimator methods.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
code/H_I_D_A_C/base_estimators/ASpearman3_estimation.m
%We use the naming convention 'A<name>_estimation' to ease embedding new association estimator methods.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
% Friedrich Shmid, Rafael Schmidt, Thomas Blumentritt, Sandra Gaiser, and Martin Ruppert. Copula Theory and Its Applications, Chapter Copula based Measures of Multivariate Association. Lecture Notes in Statistics. Springer, 2010.
code/H_I_D_A_C/base_estimators/ASpearman3_initialization.m
% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association estimator methods.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
code/H_I_D_A_C/base_estimators/CCE_kNN_k_estimation.m
+%We use the naming convention 'C<name>_estimation' to ease embedding new cross quantity estimation methods.
% Y2: Y2(:,t) is the t^th sample from the second distribution. Note: the number of samples in Y1 [=size(Y1,2)] and Y2 [=size(Y2,2)] can be different.
% Nikolai Leonenko, Luc Pronzato, and Vippal Savani. A class of Renyi information estimators for multidimensional densities. Annals of Statistics, 36(5):21532182, 2008.
code/H_I_D_A_C/base_estimators/CCE_kNN_k_initialization.m
% 2)We use the naming convention 'C<name>_initialization' to ease embedding new cross estimation methods.
+% 2)We use the naming convention 'C<name>_initialization' to ease embedding new cross quantity estimation methods.
code/H_I_D_A_C/meta_estimators/ACCIM_estimation.m
+%Estimates the centered correntropy induced metric of Y1 and Y2 (A) using the relation CCIM(y^1,y^_2) = [CCE(y^1,y^1)+CCE(y^2,y^2)2CCE(y^1,y^2)]^{1/2}, where CCE denotes centered correntropy.
+% 1)We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
+A = sqrt(abs(A_Y1Y1 + A_Y2Y2  2*A_Y1Y2));%abs(): to guarantee that the argument of sqrt is nonnegative (due to the finite number of samples)
code/H_I_D_A_C/meta_estimators/ACCIM_initialization.m
+%Initialization of the centered correntropy induced metric estimator, defined according to the relation CCIM(y^1,y^_2) = [CCE(y^1,y^1)+CCE(y^2,y^2)2CCE(y^1,y^2)]^{1/2}, where CCE denotes centered correntropy.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/meta_estimators/ACIM_estimation.m
+%Estimates the correntropy induced metric of Y1 and Y2 using the relation CIM(y^1,y^_2) = [k(0,0)correntropy(y^1,y^2)]^{1/2}, where k is the applied (Gaussian) kernel.
+%We use the naming convention 'A<name>_estimation' to ease embedding new association measure estimator methods.
+% 1)We use the naming convention 'A<name>_estimation' to ease embedding new association estimator methods.
+% Sohan Seth and Jose C. Principe. Compressed signal reconstruction using the correntropy induced metric. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 38453848, 2008.
+% Weifeng Liu, P.P. Pokharel, and Jose C. Principe. Correntropy: Properties and applications in nonGaussian signal processing. IEEE Transactions on Signal Processing, 55:52865298, 2007.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/meta_estimators/ACIM_initialization.m
+%Initialization of the correntropy induced metric estimator, defined according to the relation CIM(y^1,y^_2) = [k(0,0)correntropy(y^1,y^2)]^{1/2}, where k is the applied (Gaussian) kernel.
+% 2)We use the naming convention 'A<name>_initialization' to ease embedding new association measure estimator methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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.member_name = 'CorrEntr_KDE_direct'; %you can change it to any (Gaussian kernel based) correntropy estimator
code/H_I_D_A_C/meta_estimators/DEnergyDist_DMMD_estimation.m
% Dino Sejdinovic, Arthur Gretton, Bharath Sriperumbudur, and Kenji Fukumizu. Hypothesis testing using pairwise distances and associated kernels. International Conference on Machine Learning (ICML), pages 11111118, 2012. (semimetric space; energy distance <=> MMD, with a suitable kernel)
% Russell Lyons. Distance Covariance in metric spaces. Technical report, Indiana University, 2011. http://arxiv.org/abs/1106.5758. (energy distance, metric space of negative type; preequivalence to MMD)
+% Russell Lyons. Distance covariance in metric spaces. Annals of Probability, 2012. (To appear. http://php.indiana.edu/~rdlyons/pdf/dcov.pdf; http://arxiv.org/abs/1106.5758; energy distance, metric space of negative type; preequivalence to MMD).
% Gabor J. Szekely and Maria L. Rizzo. A new test for multivariate normality. Journal of Multivariate Analysis, 93:5880, 2005. (energy distance; metric space of negative type)
% Gabor J. Szekely and Maria L. Rizzo. Testing for equal distributions in high dimension. InterStat, 5, 2004. (energy distance; R^d)
code/H_I_D_A_C/meta_estimators/IApprCorrEntr_estimation.m
+%Estimates the approximate correntropy independence measure (I) based on centered correntropy, using the relation: I(y^1,y^2) = max(CCorrEntr(y^1,y^2),CCorrEntr(y^1,y^2)), where CCorrEntr denotes centered correntropy.
+% 1)We use the naming convention 'I<name>_estimation' to ease embedding new mutual information estimation methods.
+% Murali Rao, Sohan Seth, Jianwu Xu, Yunmei Chen, Hemant Tagare, and Jose C. Principe. A test of independence based on a generalized correlation function. Signal Processing, 91:1527, 2011.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/meta_estimators/IApprCorrEntr_initialization.m
+%Initialization of the approximate correntropy independence measure estimator. The estimated quantity is computed according to the relation: I(y^1,y^2) = max(CCorrEntr(y^1,y^2),CCorrEntr(y^1,y^2)), where CCorrEntr denotes centered correntropy.
+% 2)We use the naming convention 'I<name>_initialization' to ease embedding new mutual information estimation methods.
+%Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu")
+%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/>.
code/H_I_D_A_C/meta_estimators/IdCov_IHSIC_estimation.m
% Dino Sejdinovic, Arthur Gretton, Bharath Sriperumbudur, and Kenji Fukumizu. Hypothesis testing using pairwise distances and associated kernels. International Conference on Machine Learning (ICML), pages 11111118, 2012. (equivalence to HSIC)
% Russell Lyons. Distance Covariance in metric spaces. Technical report, Indiana University, 2011. http://arxiv.org/abs/1106.5758. (generalized distance covariance, rho_i; equivalence to HSIC)
+% Russell Lyons. Distance covariance in metric spaces. Annals of Probability, 2012. (To appear. http://php.indiana.edu/~rdlyons/pdf/dcov.pdf; http://arxiv.org/abs/1106.5758; generalized distance covariance, rho_i; equivalence to HSIC).
% Gabor J. Szekely and Maria L. Rizzo and. Brownian distance covariance. The Annals of Applied Statistics, 3:12361265, 2009. (distance covariance)
% Gabor J. Szekely, Maria L. Rizzo, and Nail K. Bakirov. Measuring and testing dependence by correlation of distances. The Annals of Statistics, 35:27692794, 2007. (distance covariance)
code/IPA/demos/estimate_ISA.m
% de_hat: in case of known subspace dimensions ('unknown_dimensions = 0') de_hat = de; else it contains the estimated subspace dimensions; ordered increasingly.
+% Jason A. Palmer and Scott Makeig. Contrast functions for independent subspace analysis. In International conference on Latent Variable Analysis and Signal Separation (LVA/ICA), pages 115122, 2012. (exciting, alternative proof idea for deflation methods)
% Zoltan Szabo, Barnabas Poczos, Andras Lorincz: Undercomplete Blind Subspace Deconvolution. Journal of Machine Learning Research 8(May):10631095, 2007. (proof; sufficient conditions for the ISA separation theorem)
% JeanFrancois Cardoso. Multidimensional independent component analysis. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pages 19411944, 1998. (conjecture)
% Lieven De Lathauwer, Bart De Moor, and Joos Vandewalle. Fetal electrocardiogram extraction by source subspace separation. In IEEE SP/Athos Workshop on HigherOrder Statistics, pages 134138, 1995. ('conjecture')