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Mixture of Generalized Gamma Density-Based Score Function for Fastica
Faculty
Science
Year:
2011
Type of Publication:
Article
Pages:
Authors:
Waheed, M. EL-Sayed, Mohamed, Osama Abdo, Abd El-aziz, M. E
DOI:
10.1155/2011/150294
Journal:
MATHEMATICAL PROBLEMS IN ENGINEERING HINDAWI PUBLISHING CORPORATION
Volume:
Research Area:
Engineering; Mathematics
ISSN
ISI:000285602500001
Keywords :
Mixture , Generalized Gamma Density-Based Score Function
Abstract:
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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