基于奇异值分解的超定盲信号分离

朱孝龙; 张贤达

基于奇异值分解的超定盲信号分离

朱孝龙^{①;张贤达},
张贤达

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出版历程
- 收稿日期: 2002-08-30
- 修回日期: 2003-03-28
- 刊出日期: 2004-03-19

Overdetermined Blind Source Separation Based on Singular Value Decomposition

Zhu Xiao-long^{①;张贤达},
Zhang Xian-da

摘要

摘要: 该文研究超定盲信号分离,即观测信号个数不少于源信号个数情况下的盲信号分离问题。作者从分离矩阵的奇异值分解出发,首先提出一种基于独立分量分析的超定盲信号分离代价函数,接着推导了一般梯度学习算法。此后,借助于相对梯度的概念,证明超定盲信号分离与通常的完备盲信号分离具有相同形式的自然梯度算法。仿真试验验证了算法的有效性。
- 盲信号分离; 独立分量分析; 相对梯度; 自然梯度; 奇异值分解
Abstract: The problem of overdetermined Blind Source Separation (BSS) where there are more mixtures than sources is considered. Beginning with the Singular Value Decompo sition (SVD) of the separation matrix, a cost function is presented based on Independent Component Analysis (ICA), and then the ordinary gradient learning algorithm is developed. Secondly, resorting to the relative gradient, it is shown that the natural gradient learning algorithm for overdetermined BSS has the same form as that for usual complete BSS, which is verified by simulation results.

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参考文献(1)

Bell A J, Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution[J].Neural Computation.1995, 7(6):1129-1159[2]Karhunen J, Joutsensalo J. Representation and separation of signals using nonlinear pca type learning[J].Neural Networks.1994, 7(1):113-127[3]Karhunen J, Pajunen J, Oja E. The nonlinear PCA criterion in blind source separation: Relations with other approaches[J].Neurocomputing.1998, 22(1):5-20[4]Comon P. Independent component analysis, a new concept? Signal Processing, 1994, 36(3): 287-314.[5]Amari S I.[J].Cichocki A, Yang H H. A new learning algorithm for blind signal separation. In D.S. Touretzky, M. C. Mozer M. E. Hasselmo (Eds.), Advance in Neural Information Processing Systems, Cambridge, MA: MIT Press.1996,:-[6]Cardoso J F, Laheld B. Equivariant adaptive source separation[J].IEEE Trans. on Signal Process ing.1996, 44(12):3017-3030[7]Yang H H, Amari S I. Adaptive on-line learning algorithms for blind separation-maximum entropy and minimum mutual information[J].Neural Computation.1997, 9(5):1457-1482[8]Amari S I. Natural gradient learning for over- and under-complete bases in ICA[J].Neural Computation.1999, 11(8):1875-1883[9]Zhang L Q, Cichocki A, Amari S I. Natural gradient algorithm for blind separation of overdetermined mixture with additive noise[J].IEEE Signal Processing Letters.1999, 6(11):293-295[10]Choi S, Cichocki A, Zhang L Q, Amari S I. Approximate maximum likelihood source separation using natural gradient. 3rd IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications, Taoyuan, Taiwan, 2001: 20-23.[11]Lee T W, Lewicki M S, Girolami M, Sejnowski T J. Blind source separation of more sources than mixtures using overcomplete representations[J].IEEE Signal Processing Letters.1999, 6(4):87-90[12]Lewicki M S, Sejnowski T J. Learning overcomplete representation[J].Neural Computation.2000,12(2):337-365[13]张贤达.信号处理中的线性代数.北京:科学出版社,1997,第6章.[14]Amari S I. Natural gradient works efficiently in learning[J].Neural Computation.1998, 10(2):251-276

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