一种峭度依赖的参数自适应盲分离算法
A Parameter Kurtosis-Dependent Flexible BSS Algorithm
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摘要: 针对超高斯与亚高斯混合信源分离算法上存在的不足，该文提出一种峭度依赖的参数自适应盲分离算法。该算法用加权双高斯模型估计超高斯与亚高斯信源分布，在自然梯度框架下，依据峭度实现模型参数自适应。通过使用混合图像对其进行验证，实验表明该算法不仅可以有效实现超高斯与亚高斯混合信源的分离，而且比已有算法具有更好的分离和收敛性能。Abstract: To overcome some shortcomings of existing algorithms which separate the mixture of super- and sub-gaussian sources, a parameter kurtosis-dependent flexible Blind Source Separation (BBS) algorithm is proposed. A weighed double Gaussian model is proposed to estimate super-Gaussian and sub-Gaussian probability density. In the framework of natural gradient, model parameter is calculated online by kurtosis. Applied to images mixing, experiment shows the proposed algorithm can successfully separate the mixture of super- and sub-gaussian images. Meanwhile experiment also shows that the proposed algorithm has better performance and convergence than existing algorithms.
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Amari S I. Natural gradient works efficiently in Learning[J].Neural Computation.1998, 10(2):251-276[2]Cardoso J F. Blind signal separation: Statistical principles[J].Proc. IEEE.1998, 86(10):2009-2025[3]Boscolo R, Vwani H P. Independent component analysis based on nonparametric density estimation[J].IEEE Trans. on Neural Networks.2004, 15(1):55-65[4]Vlassis N, Motomura Y. Efficient source adaptivity in independent component analysis[J].IEEE Trans. on Neural Networks.2001, 12(3):559-565[5]Lee T W, Girolami M, Sejnowski T J. Independent component analysis using an extended informax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Computation, 1999, 11(2): 409-433.[6]Choi S, Cichocki A, Amari S. Flexible independent component analysis. IEEE Workshop on Neural Networks for Signal Processing, Cambridge, UK, 1998: 83-92.[7]Hyvarinen A, Karhunen J, Oja E. Independent Component Analysis. New York: John Wiley, 2001: 203-208. -
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