Blind Source Separation Using Principal Component Neural Networks

Blind source separation (BSS)is approached from the second order statistics point of view. In particular, it is shown that temporal filtering by an arbitrary filter combined with PCA leads to the solution of the problem provided that the sources are colored and have different spectra. This result is demonstrated by applying a neural PCA model such as APEX to BSS problems with artificially created, randomly colored data.

[1]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[2]  Erkki Oja,et al.  Independent component analysis by general nonlinear Hebbian-like learning rules , 1998, Signal Process..

[3]  Bin Yang,et al.  Projection approximation subspace tracking , 1995, IEEE Trans. Signal Process..

[4]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[5]  Konstantinos I. Diamantaras,et al.  Asymmetric PCA neural networks for adaptive blind source separation , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[6]  Juha Karhunen,et al.  Representation and separation of signals using nonlinear PCA type learning , 1994, Neural Networks.

[7]  Shun-ichi Amari,et al.  Adaptive Online Learning Algorithms for Blind Separation: Maximum Entropy and Minimum Mutual Information , 1997, Neural Computation.

[8]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[9]  Konstantinos I. Diamantaras,et al.  Second order Hebbian neural networks and blind source separation , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[10]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[11]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[12]  S.Y. Kung,et al.  Adaptive Principal component EXtraction (APEX) and applications , 1994, IEEE Trans. Signal Process..

[13]  Sun-Yuan Kung,et al.  Cross-correlation neural network models , 1994, IEEE Trans. Signal Process..

[14]  Lang Tong,et al.  Blind identification and equalization based on second-order statistics: a time domain approach , 1994, IEEE Trans. Inf. Theory.

[15]  Terence D. Sanger,et al.  Optimal unsupervised learning in a single-layer linear feedforward neural network , 1989, Neural Networks.

[16]  Andrzej Cichocki,et al.  Stability Analysis of Learning Algorithms for Blind Source Separation , 1997, Neural Networks.