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Topic: Matlab simulation on ICA
Replies: 1   Last Post: Feb 28, 2014 4:34 AM

 Messages: [ Previous | Next ]
 Aaronne Posts: 110 Registered: 6/2/11
Matlab simulation on ICA
Posted: Jul 26, 2012 5:01 AM

Hi all,

I have encounter a problem of analyze some large data set. I have done this by ICA particularly using the Matlab FastICA package. However, I have got some results unexpected. I think there are might be two reasons:

1. My data set is not statistically independent enough.
2. My data might have some nonlinear mixing. (Maybe I am wrong but I believe ICA can only separate linearly mixed sources).

I tested using simple simulated data to demonstrate point 2 here as attached below. I test both noise free and noisy environment. I repeat the FastICA for linear, nonlinear, noisy linear, and noisy nonlinear mixed data 4 times. The results of linear case with or without noise are quite consistent, but the results of the nonlinear case are quite 'Random'.

May I ask opinion from your smart guys?

a. How to find out or justify that the data is 'not statistically independent enough'? And how to demonstrate this using simple simulated data?
b. Would it be possible to test if my data have some nonlinear mixing? Could you explain these random results when the mixture is nonlinear. And how to deal with this situation or how to mitigate the nonlinearity? (Using kernel PCA?)

%% Test: ICA can only separate linearly mixed sources
clc; clf; clear all; close all;

opt = 1; % Linear: 1; Nonlinear: 2; Linear Noisy: 3; Nonlinear Noisy: 4;

%% Create two signals
A = sin(linspace(0,50, 1000)); % A
B = cos(linspace(0,37, 1000)+5); % B
C = sin(linspace(0,20, 1000)+10); % C

%% Mixture of linear signals
if opt == 1
M1 = A-2*B+C; % mixing 1
M2 = 1.73*A+3.41*B-9.2*C; % mixing 2
M3 = 0.2*A+0.41*B-0.5*C; % mixing 3

%% Mixture of nonlinear signals
elseif opt == 2
M1 = A-2*B+C; % mixing 1
M2 = 1.73*A+3.41*B.^2-9.2*C; % mixing 2 B.^1.1
M3 = 0.2*A+0.41*B-0.5*C; % mixing 3 1000*C

%% Mixture of linear signals with white Gaussian noise
elseif opt == 3
M1 = A-2*B+C+(0.2+0.1.*randn(1000,1))'; % mixing 1
M2 = 1.73*A+3.41*B-9.2*C+(0.1+0.05.*randn(1000,1))'; % mixing 2
M3 = 0.2*A+0.41*B-0.5*C-(0.01+0.1.*randn(1000,1))'; % mixing 3

%% Mixture of nonlinear signals with white Gaussian noise
elseif opt == 4
M1 = A-2*B+C+(0.2+0.1.*randn(1000,1))'; % mixing 1
M2 = 1.73*A+3.41*B.^2-9.2*C+(0.1+0.05.*randn(1000,1))'; % mixing 2 B.^1.1
M3 = 0.2*A+0.41*B-0.5*C-(0.01+0.1.*randn(1000,1))'; % mixing 3 1000*C

end

%% Run fast ICA 4 times
ICs = zeros(12,1000);
for i = 1:4
% compute unminxing using fastICA
ICs((1+3*(i-1)):(1+3*(i-1))+2,:) = fastica([M1;M2;M3]);
end

%% Plot
figure,
subplot(3,6,1), plot(A, 'r'); % plot A
subplot(3,6,7), plot(B, 'r'); % plot B
subplot(3,6,13), plot(C, 'r'); % plot C

subplot(3,6,2), plot(M1, 'g'); % plot mixing 1
subplot(3,6,8), plot(M2, 'g'); % plot mixing 2
subplot(3,6,14), plot(M3, 'g'); % plot mixing 3

subplot(3,6,3), plot(ICs(1,:), 'r'); % plot IC 1
subplot(3,6,9), plot(ICs(2,:), 'r'); % plot IC 2
subplot(3,6,15), plot(ICs(3,:), 'r'); % plot IC 3

subplot(3,6,4), plot(ICs(4,:), 'r'); % plot IC 1
subplot(3,6,10), plot(ICs(5,:), 'r'); % plot IC 2
subplot(3,6,16), plot(ICs(6,:), 'r'); % plot IC 3

subplot(3,6,5), plot(ICs(7,:), 'r'); % plot IC 1
subplot(3,6,11), plot(ICs(8,:), 'r'); % plot IC 2
subplot(3,6,17), plot(ICs(9,:), 'r'); % plot IC 3

subplot(3,6,6), plot(ICs(10,:), 'r'); % plot IC 1
subplot(3,6,12), plot(ICs(11,:), 'r'); % plot IC 2
subplot(3,6,18), plot(ICs(12,:), 'r'); % plot IC 3

Date Subject Author
7/26/12 Aaronne
2/28/14 Hongyang Yu