[Eeglablist] Ask a question again for Laplace distribution ~ Sech^2(X) distribution???

[Stephen Hong]

Hello,

I have a question again: I want to compare the performance between Laplace distributed data and Hyperbolic functions.

 

When I use the runica ( ) for comparing the performance between the statistically independent sources with  Laplace distribution and the  statistically independent sources with the pdf: f(x)= 0.25*sech^2(0.5x), in which the default logistic algorithm is used, I don't understand why the blind separation performance of Laplace distributed data is always better than the latter. 

 

Checking their CDFs, the latter's CDF (i.e., 0.5*tanh(X)+0.5)  is identical to the logistic kernel distribution ( in runica ( ), So the latter should be better than the former (Laplace distributed data). Why the simulation results are always inconsistent.

 IS there someone who can explain this contradictious results? 

Thank you very much!

 

PS: (Our simulation methods:  We  tried 4 statistically independent Laplace distributed data and the above hyperbolic function as 4 sources, respectively. Then create 4 mixing signals using a mixing matrix A (4x4). After calling the "runica" function, we calculate the correlation coefficients between  ICs and the original source signals. 

We also tried 3 kinds of source signals (Gaussian, Laplace, Sech^()) together, the simulation results alwas show that Laplace case is better than others. We ensure the mean removed and source data with rough same varinace for each distribution before the sepeartion process.)

 

 

Stephen


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