ITE / code / H_I_D_A_C / meta_estimators / ACCIM_estimation.m

function [A] = ACCIM_estimation(Y,ds,co)
%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.
%  ds: subspace dimensions.
%  co: association measure estimator object.
%   2)This is a meta method: the centered correntropy estimator can be arbitrary.
%   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.



A_Y1Y1 = A_estimation([Y(1,:);Y(1,:)],ds,co.member_co);
A_Y2Y2 = A_estimation([Y(2,:);Y(2,:)],ds,co.member_co);
A_Y1Y2 = A_estimation([Y(1,:);Y(2,:)],ds,co.member_co);

%A =  sqrt(A_Y1Y1 + A_Y2Y2 - 2*A_Y1Y2);%theoretically
A =  sqrt(abs(A_Y1Y1 + A_Y2Y2 - 2*A_Y1Y2));%abs(): to guarantee that the argument of sqrt is non-negative (due to the finite number of samples)
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