302 BIBLIOGRAPHY Keck, I., Theis, F., Gruber, P., Lang, E., Specht, K., F<strong>in</strong>k, G., Tomé, A., and Puntonet, C. (2005). Automated cluster<strong>in</strong>g of ICA results for fMRI data analysis. In Proc. CIMED 2005, pages 211–216, Lisbon, Portugal. Keck, I., Theis, F., Gruber, P., Lang, E., Specht, K., and Puntonet, C. (2004). 3D spatial analysis of fMRI data on a word perception task. In Proc. ICA 2004, volume 3195 of LNCS, pages 977–984, Granada, Spa<strong>in</strong>. Spr<strong>in</strong>ger. Kruskal, J. (1969). Statistical Computation, chapter Toward a practical method which helps uncover the structure of a set of observations by f<strong>in</strong>d<strong>in</strong>g the l<strong>in</strong>e tranformation which optimizes a new <strong>in</strong>dex of condensation, pages 427–440. Academic Press, New York. Lee, D. and Seung, H. (1999). Learn<strong>in</strong>g the parts of objects by non-negative matrix factorization. Nature, 40:788–791. Lee, T., Lewicki, M., Girolami, M., and Sejnowski, T. (1999). Bl<strong>in</strong>d source separation of more sources than mixtures us<strong>in</strong>g overcomplete representations. IEEE Signal Process<strong>in</strong>g Letters, 6(4):87–90. Leshem, A. (1999). Source separation us<strong>in</strong>g bil<strong>in</strong>ear forms. In Proc. of the 8th Int. Conference on Higher-Order Statistical Signal Process<strong>in</strong>g. Liavas, A. P. and Regalia, P. A. (2001). On the behavior of <strong>in</strong>formation theoretic criteria for model order selection. IEEE Transactions on Signal Process<strong>in</strong>g, 49:1689–1695. L<strong>in</strong>, J. (1998). Factoriz<strong>in</strong>g multivariate function classes. In Advances <strong>in</strong> Neural Information Process<strong>in</strong>g Systems, volume 10, pages 563–569. Lutter, D., Stadlthanner, K., Theis, F., Lang, E. W., Tomé, A., Becker, B., and Vogt, T. (2006). Analyz<strong>in</strong>g gene expression profiles with ICA. In Proc. BioMED 2006, Innsbruck, Austria. Ma, C. T., D<strong>in</strong>g, Z., and Yau, S. F. (2000). A two-stage algorithm for MIMO bl<strong>in</strong>d deconvolution of nonstationary colored noise. IEEE Transactions on Signal Process<strong>in</strong>g, 48:1187–1192. MacKay, D. (2003). Information Theory, Inference, and Learn<strong>in</strong>g Algorithms. Cambridge University Press, 6.9 edition. McKeown, M., Jung, T., Makeig, S., Brown, G., K<strong>in</strong>dermann, S., Bell, A., and Sejnowski, T. (1998). <strong>Analysis</strong> of fMRI data by bl<strong>in</strong>d separation <strong>in</strong>to <strong>in</strong>dependent spatial components. Human Bra<strong>in</strong> Mapp<strong>in</strong>g, 6:160–188. Meyer-Baese, A., Gruber, P., Theis, F., and Foo, S. (2006). Bl<strong>in</strong>d source separation based on self-organiz<strong>in</strong>g neural network. Eng<strong>in</strong>eer<strong>in</strong>g Applications of Artificial Intelligence, 19:305–311. Meyer-Bäse, A., Gruber, P., Theis, F., Wismüller, A., and Ritter, H. (2005). Application of unsupervised cluster<strong>in</strong>g methods to biomedical image analysis. In Proc. WSOM 2005, pages 621–628, Paris, France.
BIBLIOGRAPHY 303 Meyer-Bäse, A., Theis, F., Lange, O., and Puntonet, C. (2004a). Tree-dependent and topographic <strong>in</strong>dependent component analysis for fMRI analysis. In Proc. ICA 2004, volume 3195 of LNCS, pages 782–789, Granada, Spa<strong>in</strong>. Spr<strong>in</strong>ger. Meyer-Bäse, A., Theis, F., Lange, O., and Wismüller, A. (2004b). Cluster<strong>in</strong>g of dependent components: A new paradigm for fMRI signal detection. In Proc. IJCNN 2004, pages 1947– 1952, Budapest, Hungary. Meyer-Bäse, A., Thümmler, V., and Theis, F. (2006). Stability analysis of an unsupervised competitive neural network. In Proc. IJCNN 2006, Vancouver, Canada. Mika, S., Schölkopf, B., Smola, A., Müller, K., Scholz, M., and Rätsch, G. (1999). Kernel pca and de-nois<strong>in</strong>g <strong>in</strong> feature spaces. In Proc. NIPS, volume 11. Mitchell, T. (1997). Mach<strong>in</strong>e Learn<strong>in</strong>g. McGraw Hill. Molgedey, L. and Schuster, H. (1994). Separation of a mixture of <strong>in</strong>dependent signals us<strong>in</strong>g time-delayed correlations. Physical Review Letters, 72(23):3634–3637. Moreau, E. (2001). A generalization of jo<strong>in</strong>t-diagonalization criteria for source separation. IEEE Transactions on Signal Process<strong>in</strong>g, 49(3):530–541. Müller, K.-R., Philips, P., and Ziehe, A. (1999). Jadetd: Comb<strong>in</strong><strong>in</strong>g higher-order statistics and temporal <strong>in</strong>formation for bl<strong>in</strong>d source separation (with noise). In Proc. of ICA 1999, pages 87–92, Aussois, France. Ogawa, S., Lee, T., Kay, A., and Tank, D. (1990). Bra<strong>in</strong> magnetic-resonance-imag<strong>in</strong>g with contrast dependent on blood oxygenation. Proc. Nat. Acad. Sci. USA, 87:9868–9872. O’Grady, P. and Pearlmutter, B. (2004). Soft-LOST: EM on a mixture of oriented l<strong>in</strong>es. In Proc. ICA 2004, volume 3195 of Lecture Notes <strong>in</strong> Computer Science, pages 430–436, Granada, Spa<strong>in</strong>. Pham, D. (2001). Jo<strong>in</strong>t approximate diagonalization of positive def<strong>in</strong>ite matrices. SIAM Journal on Matrix Anal. and Appl., 22(4):1136–1152. Pham, D.-T. and Cardoso, J. (2001). Bl<strong>in</strong>d separation of <strong>in</strong>stantaneous mixtures of nonstationary sources. IEEE Transactions on Signal Process<strong>in</strong>g, 49(9):1837–1848. Poczos, B. and Lör<strong>in</strong>cz, A. (2005). <strong>Independent</strong> subspace analysis us<strong>in</strong>g k-nearest neighborhood distances. In Proc. ICANN 2005, volume 3696 of LNCS, pages 163–168, Warsaw, Poland. Spr<strong>in</strong>ger. Schachtner, R., Lutter, D., Theis, F., Lang, E., Tomé, A., Saéz, J. G., and Puntonet, C. (2007). Bl<strong>in</strong>d matrix decomposition techniques to identify marker genes from microarrays. In Proc. ICA 2007, London, U.K. Schäfer, J. and Strimmer, K. (2005). Learn<strong>in</strong>g large-scale graphical gaussian models from genomic data. In Proc. CNET 2004, AIP Conference Proceed<strong>in</strong>gs 776.
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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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128 Chapter 7. Proc. ISCAS 2005, pa
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130 Chapter 8. Proc. NIPS 2006 Towa
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162 Chapter 11. EURASIP JASP, 2007
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186 Chapter 13. Proc. EUSIPCO 2005
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190 Chapter 13. Proc. EUSIPCO 2005
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192 Chapter 14. Proc. ICASSP 2006 S
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230 Chapter 16. Proc. ICA 2006, pag
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232 Chapter 16. Proc. ICA 2006, pag
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236 Chapter 16. Proc. ICA 2006, pag
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240 Chapter 17. IEEE SPL 13(2):96-9
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242 Chapter 17. IEEE SPL 13(2):96-9
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244 Chapter 17. IEEE SPL 13(2):96-9
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246 Chapter 18. Proc. EUSIPCO 2006
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248 Chapter 18. Proc. EUSIPCO 2006
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250 Chapter 18. Proc. EUSIPCO 2006
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- Page 306 and 307: 298 BIBLIOGRAPHY Barber, C., Dobkin
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