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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/742

Title: Bacteria Foraging Based Independent Component Analysis
Authors: Acharya, D P
Panda, G
Mishra, S
Lakshmi, Y V S
Keywords: genetic algorithms
independent component analysis
mean square error methods
signal processing
Issue Date: 2007
Publisher: IEEE
Citation: International Conference on Conference on Computational Intelligence and Multimedia Applications, 2007, 13-15 Dec. 2007 Sivakasi, Tamil Nadu, P 527 - 531
Abstract: The present paper proposes a bacteria foraging optimization based independent component analysis (BFOICA) algorithm assuming a linear noise free model. It is observed that the proposed BFOICA algorithm overcomes the long standing permutation ambiguity and recovers the independent components(IC) in a fixed order which depends on the statistical characteristics of the signals to be estimated. The paper compares the performance of BFOICA algorithm with the constrained genetic algorithm based ICA (CGAICA) and most popular fast ICA algorithm. The proposed algorithm offers comparable or even better performance compared to fast ICA algorithm and faster convergence and better mean square error performance compared to CGAICA.
Description: Copyright for the paper belongs IEEE
URI: http://dx.doi.org/10.1109/ICCIMA.2007.126
Appears in Collections:Conference Papers

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