Mathematics in Independent Component Analysis

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102 Chapter 4. Neurocomputing 64:223-234, 2005 Proceedings of the AIP Conference, American Institute of Physics, New York, 1986, pp. 206–211. [2] J.-F. Cardoso, A. Souloumiac, Blind beamforming for non-gaussian signals, IEEE Proceedings - Part F 140 (1993) 362–370. [3] P. Comon, Independent component analysis - a new concept?, Signal Processing 36 (1994) 287–314. [4] A. Bell, T. Sejnowski, An information-maximisation approach to blind separation and blind deconvolution, Neural Computation 7 (1995) 1129–1159. [5] A. Hyvärinen, E. Oja, A fast fixed-point algorithm for independent component analysis, Neural Computation 9 (1997) 1483–1492. [6] F. Theis, A. Jung, C. Puntonet, E. Lang, Linear geometric ICA: Fundamentals and algorithms, Neural Computation 15 (2003) 419–439. [7] A. Hyvärinen, J.Karhunen, E.Oja, Independent Component Analysis, John Wiley & Sons, 2001. [8] A. Cichocki, S. Amari, Adaptive blind signal and image processing, John Wiley & Sons, 2002. [9] A. Taleb, C. Jutten, Indeterminacy and identifiability of blind identification, IEEE Transactions on Signal Processing 47 (10) (1999) 2807–2820. [10] M. Babaie-Zadeh, C. Jutten, K. Nayebi, A geometric approach for separating post non-linear mixtures, in: Proc. of EUSIPCO ’02, Vol. II, Toulouse, France, 2002, pp. 11–14. [11] S. Achard, D.-T. Pham, C. Jutten, Quadratic dependence measure for nonlinear blind sources separation, in: Proc. of ICA 2003, Nara, Japan, 2003, pp. 263–268. [12] A. Ziehe, M. Kawanabe, S. Harmeling, K.-R. Müller, Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation, Journal of Machine Learning Research 4 (2003) 1319–1338. [13] F. Theis, E. Lang, Linearization identification and an application to BSS using a SOM, in: Proc. ESANN 2004, d-side, Evere, Belgium, Bruges, Belgium, 2004, pp. 205–210. [14] F. Theis, A new concept for separability problems in blind source separation, Neural Computation 16 (2004) 1827–1850. [15] F. Theis, Uniqueness of complex and multidimensional independent component analysis, Signal Processing 84 (5) (2004) 951–956. [16] M. Babaie-Zadeh, On blind source separation in convolutive and nonlinear mixtures, Ph.D. thesis, Institut National Polytechnique de Grenoble (2002). [17] R. Moddemeijer, On estimation of entropy and mutual information of continuous distributions, Signal Processing 16 (3) (1989) 233–246. [18] J. Cardoso, Multidimensional independent component analysis, in: Proc. of ICASSP ’98, Seattle, 1998. 13
Chapter 5 IEICE TF E87-A(9):2355-2363, 2004 Paper F.J. Theis and W. Nakamura. Quadratic independent component analysis. IEICE Trans. Fundamentals, E87-A(9):2355-2363, 2004 Reference (Theis and Nakamura, 2004) Summary in section 1.2.2 103
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Statistical machine learning of bio
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viii CONTENTS 3 Signal Processing 8
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Chapter 1 Statistical machine learn
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1.1. Introduction 5 auditory cortex
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S U M M A R Y T his �le contains
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298 BIBLIOGRAPHY Barber, C., Dobkin
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300 BIBLIOGRAPHY Georgiev, P. (2001
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302 BIBLIOGRAPHY Keck, I., Theis, F
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306 BIBLIOGRAPHY Theis, F. and Inou
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