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Bibliography

1
S.-I. Amari, A. Cichocki, and H.H. Yang.
A new learning algorithm for blind source separation.
In Advances in Neural Information Processing Systems 8, pages 757-763. MIT Press, Cambridge, MA, 1996.

2
A. D. Back and A. S. Weigend.
A first application of independent component analysis to extracting structure from stock returns.
Int. J. on Neural Systems, 8(4):473-484, 1998.

3
A.J. Bell and T.J. Sejnowski.
An information-maximization approach to blind separation and blind deconvolution.
Neural Computation, 7:1129-1159, 1995.

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

5
J.-F. Cardoso and B. Hvam Laheld.
Equivariant adaptive source separation.
IEEE Trans. on Signal Processing, 44(12):3017-3030, 1996.

6
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.

7
P. Comon.
Independent component analysis - a new concept?
Signal Processing, 36:287-314, 1994.

8
T. M. Cover and J. A. Thomas.
Elements of Information Theory.
John Wiley & Sons, 1991.

9
N. Delfosse and P. Loubaton.
Adaptive blind separation of independent sources: a deflation approach.
Signal Processing, 45:59-83, 1995.

10
D. L. Donoho, I. M. Johnstone, G. Kerkyacharian, and D. Picard.
Wavelet shrinkage: asymptopia?
Journal of the Royal Statistical Society ser. B, 57:301-337, 1995.

11
The FastICA MATLAB package.
Available at http://www.cis.hut.fi/projects/ica/fastica/.

12
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.

13
J.H. Friedman.
Exploratory projection pursuit.
J. of the American Statistical Association, 82(397):249-266, 1987.

14
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.

15
R. Gonzalez and P. Wintz.
Digital Image Processing.
Addison-Wesley, 1987.

16
P.J. Huber.
Projection pursuit.
The Annals of Statistics, 13(2):435-475, 1985.

17
A. Hyvärinen.
Independent component analysis in the presence of gaussian noise by maximizing joint likelihood.
Neurocomputing, 22:49-67, 1998.

18
A. Hyvärinen.
New approximations of differential entropy for independent component analysis and projection pursuit.
In Advances in Neural Information Processing Systems, volume 10, pages 273-279. MIT Press, 1998.

19
A. Hyvärinen.
Fast and robust fixed-point algorithms for independent component analysis.
IEEE Trans. on Neural Networks, 10(3):626-634, 1999.

20
A. Hyvärinen.
The fixed-point algorithm and maximum likelihood estimation for independent component analysis.
Neural Processing Letters, 10(1):1-5, 1999.

21
A. Hyvärinen.
Gaussian moments for noisy independent component analysis.
IEEE Signal Processing Letters, 6(6):145-147, 1999.

22
A. Hyvärinen.
Sparse code shrinkage: Denoising of nongaussian data by maximum likelihood estimation.
Neural Computation, 11(7):1739-1768, 1999.

23
A. Hyvärinen.
Survey on independent component analysis.
Neural Computing Surveys, 2:94-128, 1999.

24
A. Hyvärinen and E. Oja.
A fast fixed-point algorithm for independent component analysis.
Neural Computation, 9(7):1483-1492, 1997.

25
A. Hyvärinen and E. Oja.
Independent component analysis by general nonlinear Hebbian-like learning rules.
Signal Processing, 64(3):301-313, 1998.

26
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.

27
M.C. Jones and R. Sibson.
What is projection pursuit ?
J. of the Royal Statistical Society, ser. A, 150:1-36, 1987.

28
C. Jutten and J. Herault.
Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture.
Signal Processing, 24:1-10, 1991.

29
J. Karhunen, E. Oja, L. Wang, R. Vigário, and J. Joutsensalo.
A class of neural networks for independent component analysis.
IEEE Trans. on Neural Networks, 8(3):486-504, 1997.

30
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.

31
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, 11(2):417-441, 1999.

32
D. G. Luenberger.
Optimization by Vector Space Methods.
John Wiley & Sons, 1969.

33
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.

34
S. G. Mallat.
A theory for multiresolution signal decomposition: The wavelet representation.
IEEE Trans. on PAMI, 11:674-693, 1989.

35
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.

36
A. Papoulis.
Probability, Random Variables, and Stochastic Processes.
McGraw-Hill, 3rd edition, 1991.

37
B. A. Pearlmutter and L. C. Parra.
Maximum likelihood blind source separation: A context-sensitive generalization of ica.
In Advances in Neural Information Processing Systems, volume 9, pages 613-619, 1997.

38
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.

39
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.

40
R. Vigário.
Extraction of ocular artifacts from EEG using independent component analysis.
Electroenceph. clin. Neurophysiol., 103(3):395-404, 1997.

41
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 Systems 10, pages 229-235. MIT Press, 1998.

42
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.



Aapo Hyvarinen
2000-04-19