Bibliography

1

S. Amari, A. Cichocki, and H.H. Yang.
A new learning algorithm for blind source separation.
In Advances in Neural Information Processing 8, pages 757-763. MIT Press, Cambridge, MA, 1996.

2

S.-I. Amari.
Neural learning in structured parameter spaces -- natural riemannian gradient.
In Advances in Neural Information Processing 9, pages 127-133. MIT Press, Cambridge, MA, 1997.

3

S.-I. Amari and A. Cichocki.
Adaptive blind signal processing - neural network approaches.
Proceedings of the IEEE, 9, 1998.

4

J.J. Atick.
Entropy minimization: A design principle for sensory perception?
International Journal of Neural Systems, 3:81-90, 1992.
Supp. 1992.

5

Y. Bar-Ness.
Bootstrapping adaptive interference cancellers: Some practical limitations.
In The Globecom Conf., pages 1251-1255, Miami, 1982.
Paper F3.7.

6

H. B. Barlow.
Possible principles underlying the transformations of sensory messages.
In W. A. Rosenblith, editor, Sensory Communication, pages 217-234. MIT Press, 1961.

7

H. B. Barlow.
Single units and sensation: A neuron doctrine for perceptual psychology?
Perception, 1:371-394, 1972.

8

H.B. Barlow.
Unsupervised learning.
Neural Computation, 1:295-311, 1989.

9

H.B. Barlow.
What is the computational goal of the neocortex ?
In C. Koch and J.L. Davis, editors, Large-scale neuronal theories of the brain. MIT Press, Cambridge, MA, 1994.

10

H.B. Barlow, T.P. Kaushal, and G.J. Mitchison.
Finding minimum entropy codes.
Neural Computation, 1:412-423, 1989.

11

M. S. Bartlett.
A note on the multiplying factors for various chi-square approximations.
J. Roy. Stat. Soc., 16 ser B:296-298, 1989.

12

A.J. Bell and T.J. Sejnowski.
An information-maximization approach to blind separation and blind deconvolution.
Neural Computation, 7:1129-1159, 1995.

13

A.J. Bell and T.J. Sejnowski.
Learning higher-order structure of a natural sound.
Network, 7:261-266, 1996.

14

A.J. Bell and T.J. Sejnowski.
The 'independent components' of natural scenes are edge filters.
Vision Research, 37:3327-3338, 1997.

15

A. Belouchrani and J.-F. Cardoso.
Maximum likelihood source separation by the expectation-maximization technique: deterministic and stochastic implementation.
In Proc. NOLTA, pages 49-53, 1995.

16

A. Belouchrani, K. Abed Meraim, J.-F. Cardoso, and E. Moulines.
A blind source separation technique based on second order statistics.
IEEE Trans. on S.P., 45(2):434-44, 1997.

17

C. M. Bishop, M. Svensen, and C. K. I. Williams.
GTM: The generative topographic mapping.
Neural Computation, 10:215-234, 1998.

18

V. Capdevielle, Ch. Serviere, and J.Lacoume.
Blind separation of wide-band sources in the frequency domain.
In Proc. ICASSP-95, volume 3, pages 2080-2083, Detroit, Michigan, USA, May 9-12 1995.

19

J.-F. Cardoso.
Source separation using higher order moments.
In Proc. ICASSP'89, pages 2109-2112, 1989.

20

J.-F. Cardoso.
Eigen-structure of the fourth-order cumulant tensor with application to the blind source separation problem.
In Proc. ICASSP'90, pages 2655-2658, Albuquerque, NM, USA, 1990.

21

J.-F. Cardoso.
Super-symmetric decomposition of the fourth-order cumulant tensor. blind identification of more sources than sensors.
In Proc. ICASSP'91, pages 3109-3112, 1991.

22

J.-F. Cardoso.
Iterative techniques for blind source separation using only fourth-order cumulants.
In Proc. EUSIPCO, pages 739-742, Brussels, Belgium, 1992.

23

J.-F. Cardoso.
Infomax and maximum likelihood for source separation.
IEEE Letters on Signal Processing, 4:112-114, 1997.

24

J.-F. Cardoso.
Blind signal separation: statistical principles.
Proceedings of the IEEE, 9(10):2009-2025, 1998.

25

J. F. Cardoso.
Multidimensional independent component analysis.
In Proc. ICASSP'98, Seattle, WA, 1998.

26

J. F. Cardoso.
Entropic contrasts for source separation.
In S. Haykin, editor, Adaptive Unsupervised Learning. 1999.

27

J.-F. Cardoso and P. Comon.
Independent component analysis, a survey of some algebraic methods.
In Proc. ISCAS'96, volume 2, pages 93-96, 1996.

28

J.-F. Cardoso and B. Hvam Laheld.
Equivariant adaptive source separation.
IEEE Trans. on Signal Processing, 44(12):3017-3030, 1996.

29

J.-F. Cardoso and A. Souloumiac.
Blind beamforming for non Gaussian signals.
IEE Proceedings-F, 140(6):362-370, 1993.

30

A. Cichocki, R.E. Bogner, L. Moszczynski, and K. Pope.
Modified Herault-Jutten algorithms for blind separation of sources.
Digital Signal Processing, 7:80 - 93, 1997.

31

A. Cichocki, S. C. Douglas, and S.-I. Amari.
Robust techniques for independent component analysis with noisy data.
Neurocomputing, 22:113-129, 1998.

32

A. Cichocki and R. Unbehauen.
Neural Networks for Signal Processing and Optimization.
Wiley, 1994.

33

A. Cichocki and R. Unbehauen.
Robust neural networks with on-line learning for blind identification and blind separation of sources.
IEEE Trans. on Circuits and Systems, 43(11):894-906, 1996.

34

A. Cichocki, R. Unbehauen, L. Moszczynski, and E. Rummert.
A new on-line adaptive algorithm for blind separation of source signals.
In Proc. Int. Symposium on Artificial Neural Networks ISANN-94, pages 406-411, Tainan, Taiwan, 1994.

35

P. Comon.
Blind identification in presence of noise.
In Signal Processing VI: Theories and Application (Proc. EUSIPCO), pages 835-838. Elsevier, 1992.

36

P. Comon.
Independent component analysis - a new concept?
Signal Processing, 36:287-314, 1994.

37

D. Cook, A. Buja, and J. Cabrera.
Projection pursuit indexes based on orthonormal function expansions.
J. of Computational and Graphical Statistics, 2(3):225-250, 1993.

38

J. G. Daugman.
Entropy reduction and decorrelation in visual coding by oriented neural receptive fields.
IEEE Trans. on Biomedical Engineering, 36:107-114, 1989.

39

G. Deco and D. Obradovic.
Linear redundancy reduction learning.
Neural Networks, 8(5):751-755, 1995.

40

N. Delfosse and P. Loubaton.
Adaptive blind separation of independent sources: a deflation approach.
Signal Processing, 45:59-83, 1995.

41

N. Delfosse and P. Loubaton.
Adaptive blind separation of convolutive mixtures.
In Proc. ICASSP'96, pages 2940-2943, 1996.

42

D. Donoho.
On minimum entropy deconvolution.
In Applied Time Series Analysis II, pages 565-608. Academic Press, 1981.

43

S.C. Douglas, A. Cichocki, , and S. Amari.
A bias removal technique for blind source separation with noisy measurements.
Electronics Letters, 34:1379-1380, 1998.

44

D.J. Field.
What is the goal of sensory coding?
Neural Computation, 6:559-601, 1994.

45

J. H. Friedman and J. W. Tukey.
A projection pursuit algorithm for exploratory data analysis.
IEEE Trans. of Computers, c-23(9):881-890, 1974.

46

J.H. Friedman.
Exploratory projection pursuit.
J. of the American Statistical Association, 82(397):249-266, 1987.

47

C. Fyfe and R. Baddeley.
Non-linear data structure extraction using simple Hebbian networks.
Biological Cybernetics, 72:533-541, 1995.

48

X. Giannakopoulos, J. Karhunen, and E. Oja.
Experimental comparison of neural ICA algorithms.
In Proc. Int. Conf. on Artificial Neural Networks (ICANN'98), pages 651-656, Skövde, Sweden, 1998.

49

M. Girolami and C. Fyfe.
An extended exploratory projection pursuit network with linear and nonlinear anti-hebbian connections applied to the cocktail party problem.
Neural Networks, 10:1607-1618, 1997.

50

F.R. Hampel, E.M. Ronchetti, P.J. Rousseuw, and W.A. Stahel.
Robust Statistics.
Wiley, 1986.

51

H. H. Harman.
Modern Factor Analysis.
University of Chicago Press, 2nd edition, 1967.

52

T. Hastie and W. Stuetzle.
Principal curves.
Journal of the American Statistical Association, 84:502-516, 1989.

53

S. Haykin, editor.
Blind Deconvolution.
Prentice-Hall, 1994.

54

S. Haykin.
Adaptive Filter Theory.
Prentice-Hall International, 3rd edition, 1996.

55

R. Hecht-Nielsen.
Replicator neural networks for universal optimal source coding.
Science, 269:1860-1863, 1995.

56

P.J. Huber.
Robust Statistics.
Wiley, 1981.

57

P.J. Huber.
Projection pursuit.
The Annals of Statistics, 13(2):435-475, 1985.

58

J. Hurri, A. Hyvärinen, and E. Oja.
Wavelets and natural image statistics.
In Proc. Scandinavian Conf. on Image Analysis '97, Lappenranta, Finland, 1997.

59

A. Hyvärinen.
Purely local neural principal component and independent component learning.
In Proc. Int. Conf. on Artificial Neural Networks, pages 139-144, Bochum, Germany, 1996.

60

A. Hyvärinen.
A family of fixed-point algorithms for independent component analysis.
In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), pages 3917-3920, Munich, Germany, 1997.

61

A. Hyvärinen.
Independent component analysis by minimization of mutual information.
Technical Report A46, Helsinki University of Technology, Laboratory of Computer and Information Science, 1997.

62

A. Hyvärinen.
One-unit contrast functions for independent component analysis: A statistical analysis.
In Neural Networks for Signal Processing VII (Proc. IEEE Workshop on Neural Networks for Signal Processing), pages 388-397, Amelia Island, Florida, 1997.

63

A. Hyvärinen.
Independent component analysis in the presence of gaussian noise by maximizing joint likelihood.
Neurocomputing, 22:49-67, 1998.

64

A. Hyvärinen.
New approximations of differential entropy for independent component analysis and projection pursuit.
In Advances in Neural Information Processing Systems 10, pages 273-279. MIT Press, 1998.

65

A. Hyvärinen.
Fast and robust fixed-point algorithms for independent component analysis.
IEEE Trans. on Neural Networks, 1999.
To appear.

66

A. Hyvärinen.
Fast independent component analysis with noisy data using gaussian moments.
In Proc. Int. Symp. on Circuits and Systems, Orlando, Florida, 1999.
To appear.

67

A. Hyvärinen.
The fixed-point algorithm and maximum likelihood estimation for independent component analysis.
Neural Processing Letters, 1999.
To appear.

68

A. Hyvärinen.
Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation.
Neural Computation, 1999.
Submitted.

69

A. Hyvärinen, R. Cristescu, and E. Oja.
A fast algorithm for estimating overcomplete ICA bases for image windows.
In Proc. Int. Joint Conf. on Neural Networks, Washington, D.C., 1999.

70

A. Hyvärinen and P. Hoyer.
Independent subspace analysis shows emergence of phase and shift invariant features from natural images.
In Proc. Int. Joint Conf. on Neural Networks, Washington, D.C., 1999.

71

A. Hyvärinen and E. Oja.
Simple neuron models for independent component analysis.
Int. Journal of Neural Systems, 7(6):671-687, 1996.

72

A. Hyvärinen and E. Oja.
A fast fixed-point algorithm for independent component analysis.
Neural Computation, 9(7):1483-1492, 1997.

73

A. Hyvärinen and E. Oja.
Independent component analysis by general nonlinear Hebbian-like learning rules.
Signal Processing, 64(3):301-313, 1998.

74

A. Hyvärinen, E. Oja, P. Hoyer, and J. Hurri.
Image feature extraction by sparse coding and independent component analysis.
In Proc. Int. Conf. on Pattern Recognition (ICPR'98), pages 1268-1273, Brisbane, Australia, 1998.

75

A. Hyvärinen and P. Pajunen.
Nonlinear independent component analysis: Existence and uniqueness results.
Neural Networks, 12(3):429-439, 1999.

76

A. Hyvärinen, J. Särelä, and R. Vigário.
Spikes and bumps: Artefacts generated by independent component analysis with insufficient sample size.
In Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), pages 425-429, Aussois, France, 1999.

77

I.T. Jolliffe.
Principal Component Analysis.
Springer-Verlag, 1986.

78

M.C. Jones and R. Sibson.
What is projection pursuit ?
J. of the Royal Statistical Society, ser. A, 150:1-36, 1987.

79

C. Jutten.
Calcul neuromimétique et traitement du signal, analyse en composantes indépendentes.
PhD thesis, INPG, Univ. Grenoble, 1987.
(in French).

80

C. Jutten and J. Herault.
Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture.
Signal Processing, 24:1-10, 1991.

81

J. Karhunen, A. Hyvärinen, R. Vigario, J. Hurri, and E. Oja.
Applications of neural blind separation to signal and image processing.
In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), pages 131-134, Munich, Germany, 1997.

82

J. Karhunen and J. Joutsensalo.
Representation and separation of signals using nonlinear PCA type learning.
Neural Networks, 7(1):113-127, 1994.

83

J. Karhunen and J. Joutsensalo.
Generalizations of principal component analysis, optimization problems, and neural networks.
Neural Networks, 8(4):549-562, 1995.

84

J. Karhunen, E. Oja, L. Wang, R. Vigario, and J. Joutsensalo.
A class of neural networks for independent component analysis.
IEEE Trans. on Neural Networks, 8(3):486-504, 1997.

85

J. Karhunen and P. Pajunen.
Blind source separation using least-squares type adaptive algorithms.
In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), pages 3048-3051, Munich, Germany, 1997.

86

J. Karhunen, P. Pajunen, and E. Oja.
The nonlinear PCA criterion in blind source separation: Relations with other approaches.
Neurocomputing, 22:5-20, 1998.

87

M. Kendall.
Multivariate Analysis.
Charles Griffin&Co., 1975.

88

M. Kendall and A. Stuart.
The Advanced Theory of Statistics.
Charles Griffin & Company, 1958.

89

K. Kiviluoto and E. Oja.
Independent component analysis for parallel financial time series.
In Proc. ICONIP'98, volume 2, pages 895-898, Tokyo, Japan, 1998.

90

T. Kohonen.
Self-Organizing Maps.
Springer-Verlag, Berlin, Heidelberg, New York, 1995.

91

Beate Laheld and Jean-François Cardoso.
Adaptive source separation with uniform performance.
In Proc. EUSIPCO, pages 183-186, Edinburgh, 1994.

92

R. H. Lambert.
Multichannel Blind Deconvolution: FIR Matrix Algebra and Separation of Multipath Mixtures.
PhD thesis, Univ. of Southern California, 1996.

93

L. De Lathauwer, B. De Moor, and J. Vandewalle.
A technique for higher-order-only blind source separation.
In Proc. ICONIP, Hong Kong, 1996.

94

D. N. Lawley.
Test of significance of the latent roots of the covariance and correlation matrices.
Biometrica, 43:128-136, 1956.

95

T.-W. Lee, M. Girolami, A.J. Bell, and T.J. Sejnowski.
A unifying information-theoretic framework for independent component analysis.
International Journal on Mathematical and Computer Models, 1999.
To appear.

96

T.-W. Lee, M. Girolami, and T. J. Sejnowski.
Independent component analysis using an extended infomax algorithm for mixed sub-gaussian and super-gaussian sources.
Neural Computation, pages 609-633, 1998.
11.

97

T-W. Lee, B.U. Koehler, and R. Orglmeister.
Blind source separation of nonlinear mixing models.
In Neural networks for Signal Processing VII, pages 406-415, 1997.

98

M. Lewicki and B. Olshausen.
Inferring sparse, overcomplete image codes using an efficient coding framework.
In Advances in Neural Information Processing 10 (Proc. NIPS*97), pages 815-821. MIT Press, 1998.

99

M. Lewicki and T. J. Sejnowski.
Learing overcomplete representations.
In Advances in Neural Information Processing 10 (Proc. NIPS*97), pages 556-562. MIT Press, 1998.

100

U. Lindgren, T. Wigren, and H. Broman.
On local convergence of a class of blind separation algorithms.
IEEE Trans. on Signal Processing, 43:3054-3058, 1995.

101

S. Makeig, A.J. Bell, T.-P. Jung, and T.-J. Sejnowski.
Independent component analysis of electroencephalographic data.
In Advances in Neural Information PRocessing Systems 8, pages 145-151. MIT Press, 1996.

102

S. G. Mallat.
A theory for multiresolution signal decomposition: The wavelet representation.
IEEE Trans. on PAMI, 11:674-693, 1989.

103

Z. Malouche and O. Macchi.
Extended anti-Hebbian adaptation for unsupervised source extraction.
In Proc. ICASSP'96, pages 1664-1667, Atlanta, Georgia, 1996.

104

M. McKeown, S. Makeig, S. Brown, T.-P. Jung, S. Kindermann, A.J. Bell, V. Iragui, and T. Sejnowski.
Blind separation of functional magnetic resonance imaging (fMRI) data.
Human Brain Mapping, 6(5-6):368-372, 1998.

105

L. Molgedey and H. G. Schuster.
Separation of a mixture of independent signals using time delayed correlations.
Phys. Rev. Lett., 72:3634-3636, 1994.

106

E. Moreau and O. Macchi.
New self-adaptive algorithms for source separation based on contrast functions.
In Proc. IEEE Signal Processing Workshop on Higher Order Statistics, pages 215-219, Lake Tahoe, USA, June 1993.

107

E. Moulines, J.-F. Cardoso, and E. Gassiat.
Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models.
In Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP'97), pages 3617-3620, Munich, Germany, 1997.

108

J.-P. Nadal and N. Parga.
Non-linear neurons in the low noise limit: a factorial code maximizes information transfer.
Network, 5:565-581, 1994.

109

C. Nikias and J. Mendel.
Signal processing with higher-order spectra.
IEEE Signal Processing Magazine, pages 10-37, July 1993.

110

E. Oja.
A simplified neuron model as a principal component analyzer.
J. of Mathematical Biology, 15:267-273, 1982.

111

E. Oja.
Neural networks, principal components, and subspaces.
Int. J. on Neural Systems, 1:61-68, 1989.

112

E. Oja.
The nonlinear PCA learning rule in independent component analysis.
Neurocomputing, 17(1):25-46, 1997.

113

E. Oja.
Nonlinear PCA criterion and maximum likelihood in independent component analysis.
In Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), pages 143-148, Aussois, France, 1999.

114

E. Oja and J. Karhunen.
On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix.
Journal of Math. Analysis and Applications, 106:69-84, 1985.

115

E. Oja, H. Ogawa, and J. Wangviwattana.
Learning in nonlinear constrained Hebbian networks.
In T. Kohonen et al., editor, Artificial Neural Networks, Proc. ICANN'91, pages 385-390, Espoo, Finland, 1991. North-Holland, Amsterdam.

116

B. A. Olshausen and D. J. Field.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images.
Nature, 381:607-609, 1996.

117

B. A. Olshausen and D. J. Field.
Natural image statistics and efficient coding.
Network, 7(2):333-340, May 1996.

118

B. A. Olshausen and D. J. Field.
Sparse coding with an overcomplete basis set: A strategy employed by V1?
Vision Research, 37:3311-3325, 1997.

119

P. Pajunen.
Blind source separation using algorithmic information theory.
Neurocomputing, 22:35-48, 1998.

120

P. Pajunen, A. Hyvärinen, and J. Karhunen.
Nonlinear blind source separation by self-organizing maps.
In Proc. Int. Conf. on Neural Information Processing, pages 1207-1210, Hong Kong, 1996.

121

P. Pajunen and J. Karhunen.
A maximum likelihood approach to nonlinear blind source separation.
In Proceedings of the 1997 Int. Conf. on Artificial Neural Networks (ICANN'97), pages 541-546, Lausanne, Switzerland, 1997.

122

Athanasios Papoulis.
Probability, Random Variables, and Stochastic Processes.
McGraw-Hill, 3rd edition, 1991.

123

B.A. Pearlmutter and L.C. Parra.
A context-sensitive generalization of ICA.
In Proc. ICONIP'96, pages 151-157, Hong Kong, 1996.

124

D.-T. Pham, P. Garrat, and C. Jutten.
Separation of a mixture of independent sources through a maximum likelihood approach.
In Proc. EUSIPCO, pages 771-774, 1992.

125

T. Ristaniemi and J. Joutsensalo.
On the performance of blind source separation in CDMA downlink.
In Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), pages 437-441, Aussois, France, 1999.

126

Y. Sato.
A method for self-recovering equalization for multilevel amplitude-modulation system.
IEEE Trans. on Communications, 23:679-682, 1975.

127

M. Schervish.
Theory of Statistics.
Springer, 1995.

128

J. Schmidhuber, M. Eldracher, and B. Foltin.
Semilinear predictability minimization produces well-known feature detectors.
Neural Computation, 8:773-786, 1996.

129

O. Shalvi and E. Weinstein.
New criteria for blind deconvolution of nonminimum phase systems (channels).
IEEE Trans. on Information Theory, 36(2):312-321, 1990.

130

O. Shalvi and E. Weinstein.
Super-exponential methods for blind deconvolution.
IEEE Trans. on Information Theory, 39(2):504:519, 1993.

131

E. Sorouchyari.
Blind separation of sources, Part III: Stability analysis.
Signal Processing, 24:21-29, 1991.

132

J. Sun.
Some practical aspects of exploratory projection pursuit.
SIAM J. of Sci. Comput., 14:68-80, 1993.

133

A. Taleb and C. Jutten.
Nonlinear source separation: The postlinear mixtures.
In Proc. European Symposium on Artificial Neural Networks, pages 279-284, Bruges, Belgium, 1997.

134

H.-L. Nguyen Thi and C. Jutten.
Blind source separation for convolutive mixtures.
Signal Processing, 45:209-229, 1995.

135

L. Tong, R.-W. Liu, V.C. Soon, and Y.-F. Huang.
Indeterminacy and identifiability of blind identification.
IEEE Trans. on Circuits and Systems, 38, 1991.

136

L. Tong, V. Soo, R. Liu, and Y. Huang.
Amuse: a new blind identification algorithm.
In Proc. ISCAS, New Orleans, USA, 1990.

137

K. Torkkola.
Blind separation of delayed sources based on information maximization.
In Proc. ICASSP'96, pages 3509-3512, Atlanta, Georgia, 1996.

138

J. H. van Hateren and D. L. Ruderman.
Independent component analysis of natural image sequences yields spatiotemporal filters similar to simple cells in primary visual cortex.
Proc. Royal Society ser. B, 265:2315-2320, 1998.

139

J. H. van Hateren and A. van der Schaaf.
Independent component filters of natural images compared with simple cells in primary visual cortex.
Proc. Royal Society ser. B, 265:359-366, 1998.

140

R. Vigário.
Extraction of ocular artifacts from EEG using independent component analysis.
Electroenceph. clin. Neurophysiol., 103(3):395-404, 1997.

141

R. Vigário, V. Jousmäki, M. Hämäläinen, R. Hari, and E. Oja.
Independent component analysis for identification of artifacts in magnetoencephalographic recordings.
In Advances in Neural Information Processing 10 (Proc. NIPS'97), pages 229-235, Cambridge, MA, 1998. MIT Press.

142

R. Vigário, J. Särelä, and E. Oja.
Independent component analysis in wave decomposition of auditory evoked fields.
In Proc. Int. Conf. on Artificial Neural Networks (ICANN'98), pages 287-292, Skövde, Sweden, 1998.

143

R. Vigário, J. Särelä, V. Jousmäki, and E. Oja.
Independent component analysis in decomposition of auditory and somatosensory evoked fields.
In Proc. Int. Workshop on Independent Component Analysis and Signal Separation (ICA'99), pages 167-172, Aussois, France, 1999.

144

H. Wang and M. Kaveh.
Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sources.
IEEE Trans. on ASSP, 33:823-831, 1985.

145

L.-Y. Wang and J. Karhunen.
A unified neural bigradient algorithm for robust PCA and MCA.
Int. J. of Neural Systems, 7(1):53-67, 1996.

146

M. Wax and T. Kailath.
Detection of signals by information-theoretic criteria.
IEEE Trans. on ASSP, 33:387-392, 1985.

147

E. Weinstein, M. Feder, and A. V. Oppenheim.
Multi-channel signal separation by decorrelation.
IEEE Trans. on SAP, 1:405-413, 1993.

148

R. A. Wiggins.
Minimum entropy deconvolution.
Geoexploration, 16:12-35, 1978.

149

D. Yellin and E. Weinstein.
Criteria for multichannel signal separation.
IEEE Trans. on Signal Processing, 42:2158-2167, 1994.

150

D. Yellin and E. Weinstein.
Multichannel signal separation: Methods and analysis.
IEEE Trans. on Signal Processing, 44:106-118, 1996.