Overdetermined Blind Source Separation Based on Singular Value Decomposition

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