Mathematics in Independent Component Analysis

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298 BIBLIOGRAPHY Barber, C., Dobkin, D., and Huhdanpaa, H. (1993). The quickhull algorithm for convex hull. Technical Report GCG53, The Geometry Center, University of Minnesota, Minneapolis. Bartsch, H. and Obermayer, K. (2003). Second order statistics of natural images. Neurocomputing, 52-54:467–472. Basmajian, J. and Luca, C. D. (1985). Muscle Alive. Their Functions Revealed by Electromyography. Williams & Wilkins, Baltimore, 5th edition. Bell, A. and Sejnowski, T. (1995). An information-maximisation approach to blind separation and blind deconvolution. Neural Computation, 7:1129–1159. Belouchrani, A. and Amin, M. (1998). Blind source separation based on time-frequency signal representations. IEEE Trans. Signal Processing, 46(11):2888–2897. Belouchrani, A., Meraim, K. A., Cardoso, J.-F., and Moulines, E. (1997). A blind source separation technique based on second order statistics. IEEE Transactions on Signal Processing, 45(2):434–444. Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford University Press. Bishop, C. (2007). Pattern Recognition and Machine Learning. Springer. Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V., and Müller, K. (2006). In search of non-gaussian components of a high-dimensional distribution. Journal of Machine Learning Research, 7:247–282. Bofill, P. and Zibulevsky, M. (2001). Underdetermined blind source separation using sparse representations. Signal Processing, 81:2353–2362. Böhm, M., Stadlthanner, K., Gruber, P., Theis, F., Lang, E., Tomé, A., Teixeira, A., Gronwald, W., and Kalbitzer, H. (2006). On the use of simulated annealing to automatically assign independent components. IEEE Transactions on Biomedical Engineering, 53(5):810–820. Boser, B., Guyon, I., and Vapnik, V. (1992). A training algorithm for optimal margin classifiers. In Proc. COLT 1992, pages 144–152. Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Knowledge Discovery and Data Mining, 2(2):121–167. Cardoso, J. (1998). Multidimensional independent component analysis. In Proc. of ICASSP ’98, Seattle. Cardoso, J. and Souloumiac, A. (1990). Localization and identification with the quadricovariance. Traitement du Signal, 7(5):397–406. Cardoso, J. and Souloumiac, A. (1993). Blind beamforming for non gaussian signals. IEE Proceedings - F, 140(6):362–370.
BIBLIOGRAPHY 299 Cardoso, J. and Souloumiac, A. (1995). Jacobi angles for simultaneous diagonalization. SIAM J. Mat. Anal. Appl., 17(1):161–164. Choi, S. and Cichocki, A. (2000). Blind separation of nonstationary sources in noisy mixtures. Electronics Letters, 36(848-849). Cichocki, A. and Amari, S. (2002). Adaptive blind signal and image processing. John Wiley & Sons. Cohen, L. (1995). Time-Frequency Analysis. Prentice-Hall, Englewood Cliffs, NJ. Combettes, P. (1993). The foundations of set theoretic estimation. Proc. IEEE, 81:182–208. Comon, P. (1994). Independent component analysis - a new concept? Signal Processing, 36:287– 314. Darmois, G. (1953). Analyse générale des liaisons stochastiques. Rev. Inst. Internationale Statist., 21:2–8. Dobra, A., Hans, C., Jones, B., Nevins, J., Yao, G., and West, M. (2004). Sparse graphical models for exploring gene expression data. Journal of Multivariate Analysis, 90:196–212. Edelman, A., Arias, T., and Smith, S. (1999). The geometry of algorithms with orthogonality constraints. SIAM Journal on Matrix Analysis and Applications, 20(2):303–353. Effern, A., Lehnertz, K., Schreiber, T., Grunwald, T., David, P., and Elger, C. E. (2000). Nonlinear denoising of transient signals with application to event-related potentials. Physica D, 140:257–266. Eriksson, J. and Koivunen, V. (2003). Identifiability and separability of linear ica models revisited. In Proc. of ICA 2003, pages 23–27. Eriksson, J. and Koivunen, V. (2006). Complex random vectors and ica models: identifiability, uniqueness, and separability. IEEE Transactions on Information Theory, 52(3):1017–1029. Févotte, C. and Theis, F. (2007a). Orthonormal approximate joint block-diagonalization. Technical report, GET/Télécom Paris. Févotte, C. and Theis, F. (2007b). Pivot selection strategies in jacobi joint block-diagonalization. In Proc. ICA 2007, London, U.K. Friedman, J. (1987). Exploratory projection pursuit. Journal of the American Statistical Association, 82:249–266. Friedman, J. and Tukey, J. (1975). A projection pursuit algorithm for exploratory data analysis. IEEE Trans. on Computers, 23(9):881–890. García, G., Okuno, R., and Akazawa, K. (2004). Decomposition algorithm for surface electrodearray electromyogram in voluntary isometric contraction. IEEE Engineering in Medicine and Biology Magazine, in press.
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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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