Abstract
FastICA and CuBICA are two remarkable independent component analysis algorithms for dealing with blind signal separation problems. In this paper, we first present a novel ICA estimation algorithm, initialization constrained FastICA (IC-FastICA), through combining the technical merits of these two approaches. Then, a performance comparison study on these three approaches is conducted through the simulations on some standard benchmark data. The experimental results demonstrate that the IC-FastICA achieves higher performances on unmixing error and signal noise ratio while appreciably increasing computation cost.
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Wang, B., Lu, W. (2005). An In-depth Comparasion on FastICA, CuBICA and IC-FastICA. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_60
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DOI: https://doi.org/10.1007/11539117_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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