Mixture of Generalized Gamma Density-Based Score Function for Fastica | Zendy

M. El-Sayed Waheed | Zendy; Mohamed Abd Elaziz | Zendy

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Mixture of Generalized Gamma Density-Based Score Function for Fastica

Author(s) -

M. El-Sayed Waheed,

Mohamed Abd Elaziz

Publication year - 2010

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2011/150294

Subject(s) - generalized gamma distribution , generalized normal distribution , mathematics , score , fastica , probability density function , laplace transform , range (aeronautics) , gamma distribution , gaussian , generalized integer gamma distribution , mixture model , algorithm , pattern recognition (psychology) , statistics , artificial intelligence , blind signal separation , computer science , normal distribution , mathematical analysis , engineering , computer network , channel (broadcasting) , physics , quantum mechanics , aerospace engineering

We propose an entirely novel family of score functions for blind signal separation (BSS), based on the family of mixture generalized gamma density which includes generalized gamma, Weilbull, gamma, and Laplace and Gaussian probability density functions. To blindly extract the independent source signals, we resort to the FastICA approach, whilst to adaptively estimate the parameters of such score functions, we use Nelder-Mead for optimizing the maximum likelihood (ML) objective function without relaying on any derivative information. Our experimental results with source employing a wide range of statistics distribution show that Nelder-Mead technique produce a good estimation for the parameters of score functions

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