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A Michigan-like immune-inspired framework for performing independent component analysis over Galois fields of prime order

2013; Elsevier BV; Volume: 96; Linguagem: Inglês

10.1016/j.sigpro.2013.09.004

1872-7557

Daniel G. Silva, Everton Z. Nadalin, Guilherme Palermo Coelho, Leonardo Tomazeli Duarte, Ricardo Suyama, Romis Attux, Fernando J. Von Zuben, Jugurta Montalvão,

Machine Learning in Bioinformatics

In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem — associated with a minimal entropy configuration — adopting a Michigan-like population structure. The simulation results reveal that the strategy is capable of reaching a performance similar to that of standard methods for lower-dimensional instances with the advantage of also handling scenarios with an elevated number of sources.