Mathematics in Independent Component Analysis

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154 Chapter 9. Neurocomputing (in press), 2007 [13] J. Cardoso, A. Souloumiac, Jacobi angles for simultaneous diagonalization, SIAM J. Mat. Anal. Appl. 17 (1) (1995) 161–164. [14] M. Joho, H. Mathis, R. Lamber, Overdetermined blind source separation: using more sensors than source signals in a noisy mixture, in: Proc. of ICA 2000, Helsinki, Finland, 2000, pp. 81–86. [15] M. McKeown, T. Jung, S. Makeig, G. Brown, S. Kindermann, A. Bell, T. Sejnowski, Analysis of fMRI data by blind separation into independent spatial components, Human Brain Mapping 6 (1998) 160–188. [16] I. Keck, F. Theis, P. Gruber, E. Lang, K. Specht, C. Puntonet, 3D spatial analysis of fMRI data on a word perception task, in: Proc. ICA 2004, Vol. 3195 of LNCS, Springer, Granada, Spain, 2004, pp. 977–984. [17] R. Woods, S. Cherry, J. Mazziotta, Rapid automated algorithm for aligning and reslicing pet images, Journal of Computer Assisted Tomography 16 (1992) 620–633. [18] A. Hyvärinen, E. Oja, A fast fixed-point algorithm for independent component analysis, Neural Computation 9 (1997) 1483–1492. [19] S. Choi, A. Cichocki, Blind separation of nonstationary sources in noisy mixtures, Electronics Letters 36 (848-849). [20] D.-T. Pham, J. Cardoso, Blind separation of instantaneous mixtures of nonstationary sources, IEEE Transactions on Signal Processing 49 (9) (2001) 1837–1848. 15
Chapter 10 IEEE TNN 16(4):992-996, 2005 Paper P. Georgiev, F.J. Theis, and A. Cichocki. Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Transactions on Neural Networks, 16(4):992-996, 2005 Reference (Georgiev et al., 2005c) Summary in section 1.4.1 155
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Statistical machine learning of bio
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to Jakob
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vi Preface
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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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1.2. Uniqueness issues in independe
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1.2. Uniqueness issues in independe
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S U M M A R Y T his �le contains
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1.3. Dependent component analysis 1
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Table 1.1: BSS algorithms based on
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1.4. Sparseness 25 1.4 Sparseness O
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1.4. Sparseness 27 Theorem 1.4.3 (S
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1.4. Sparseness 29 R 3 R 3 A BSRA R
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1.4. Sparseness 31 Sparse projectio
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1.7. Outlook 51 noise data signal i
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Part II Papers 53
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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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304 BIBLIOGRAPHY Schießl, I., Sch
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306 BIBLIOGRAPHY Theis, F. and Inou
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Mathematics in Independent Component Analysis

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