ITE / code / H_I_D_A_C / base_estimators / ACCorrEntr_KDE_Lapl_estimation.m

function [A] = ACCorrEntr_KDE_Lapl_estimation(Y,ds,co)
%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.
%
%INPUT:
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: association measure estimator object.
%
%REFERENCE:
%   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, 91:15-27, 2011.
%   Aiyou Chen. Fast kernel density independent component analysis. In Independent Component Analysis and Blind Signal Separation (ICA), pages 24-31, 2006. (sorting based trick)
%
%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.

%verification:
    if sum(ds) ~= size(Y,1);
        error('The subspace dimensions are not compatible with Y.');
    end
    if ~one_dimensional_problem(ds) || length(ds)~=2
        error('There must be 2 pieces of one-dimensional subspaces (coordinates) for this estimator.');
    end
    
A = centcorrenexp(Y(1,:).',Y(2,:).',co.sigma);
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