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Adaptive Step-Size Natural Gradient ICA Algorithm with Weighted Orthogonalization

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Abstract

To improve the stability of the traditional natural gradient independent component analysis (ICA) algorithm and the accuracy of its separated results, a adaptive step-size natural gradient ICA algorithm with weighted orthogonalization is proposed. First, to take advantage of the pre-whitening pre-processing and keep the equivariance property of the ICA algorithm, based on the weighted orthogonal constraint on the separating matrix without pre-whitening of observed signals, weighted orthogonalization is introduced after the traditional gradient update. Then, according to the error estimation from the smoothed distance between separated outputs and optimal outputs, we obtain two adaptive step sizes based, respectively, on an unconstrained natural gradient ICA process and a weighted orthogonalization process. Simulation experiment results show that the speed of convergence of the adaptive step-size natural gradient ICA algorithms with weighted orthogonalization are faster than the traditional one; also, the stability of the algorithms and the accuracy of the separated results are improved observably.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under grants 61075117, 11101322.

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Authors and Affiliations

  1. School of Science, Xidian University, Xi’an, 710071, China

    Xingjia Tang, Xiufang Zhang & Jimin Ye

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  1. Xingjia Tang
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  2. Xiufang Zhang
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  3. Jimin Ye
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Correspondence to Xingjia Tang.

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Tang, X., Zhang, X. & Ye, J. Adaptive Step-Size Natural Gradient ICA Algorithm with Weighted Orthogonalization. Circuits Syst Signal Process 33, 211–221 (2014). https://doi.org/10.1007/s00034-013-9624-1

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  • DOI: https://doi.org/10.1007/s00034-013-9624-1

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