Abstract
Independent Component Analysis (ICA) is a statistical computation method that transforms a random vector in another one whose components are independent. Because the marginal distributions are usually unknown, the final problem is reduced to an optimization of a contrast function, a function that measures the independence of the components. In this paper, the stochastic global Particle Swarm Optimization (PSO) algorithm is used to solve the optimization problem. The PSO is used to separate some selected benchmarks signals based on two different contrast functions. The results obtained using the PSO are compared with classical ICA algorithms. It is shown that the PSO is a more powerful and robust technique and capable of finding the original signals or sources when classical ICA algorithms give poor results or fail to converge.
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Igual, J., Ababneh, J., Llinares, R., Miro-Borras, J., Zarzoso, V. (2010). Solving Independent Component Analysis Contrast Functions with Particle Swarm Optimization. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_63
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DOI: https://doi.org/10.1007/978-3-642-15822-3_63
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