ITE / code / H_I_D_A_C / meta_estimators / ACIM_estimation.m

function [A] = ACIM_estimation(Y,ds,co)
%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.
%   Y: Y(:,t) is the t^th sample.
%  ds: subspace dimensions.
%  co: association measure estimator object.
%   1)We use the naming convention 'A<name>_estimation' to ease embedding new association estimator methods.
%   2)This is a meta method: the (Gaussian) correntropy estimator can be arbitrary.
%   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 3845-3848, 2008.
%   Weifeng Liu, P.P. Pokharel, and Jose C. Principe. Correntropy: Properties and applications in non-Gaussian signal processing. IEEE Transactions on Signal Processing, 55:5286-5298, 2007.
%Copyright (C) 2012 Zoltan Szabo ("", "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 <>.


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

A0 = A_estimation(Y,ds,co.member_co); %correntropy
k0 = 1; %Assumption: k(u,u)=1. Example (Gaussian kernel): k(u,v)=e^{-(u-v)^2}, k(u,u)=1
A =  sqrt(k0-A0);