306 BIBLIOGRAPHY Theis, F. and Inouye, Y. (2006). On the use of jo<strong>in</strong>t diagonalization <strong>in</strong> bl<strong>in</strong>d signal process<strong>in</strong>g. In Proc. ISCAS 2006, Kos, Greece. Theis, F., Jung, A., Puntonet, C., and Lang, E. (2003). L<strong>in</strong>ear geometric ICA: Fundamentals and algorithms. Neural Computation, 15:419–439. Theis, F. and Kawanabe, M. (2006). Uniqueness of non-gaussian subspace analysis. In Proc. ICA 2006, pages 917–925, Charleston, USA. Theis, F. and Kawanabe, M. (2007). Colored subspace analysis. In Proc. ICA 2007, London, U.K. Theis, F., Kohl, Z., Guggenberger, C., Kuhn, H., Puntonet, C., and Lang, E. (2004b). ZANE - an algorithm for count<strong>in</strong>g labelled cells <strong>in</strong> section images. In Proc. MEDSIP 2004, Malta. Theis, F., Kohl, Z., Kuhn, H., Stockmeier, H., and Lang, E. (2004c). Automated count<strong>in</strong>g of labelled cells <strong>in</strong> rodent bra<strong>in</strong> section images. In Proc. BioMED 2004, pages 209–212, Innsbruck, Austria. ACTA Press, Canada. Theis, F. and Lang, E. (2004a). L<strong>in</strong>earization identification and an application to BSS us<strong>in</strong>g a SOM. In Proc. ESANN 2004, pages 205–210, Bruges, Belgium. d-side, Evere, Belgium. Theis, F. and Lang, E. (2004b). Postnonl<strong>in</strong>ear bl<strong>in</strong>d source separation via l<strong>in</strong>earization identification. In Proc. IJCNN 2004, pages 2199–2204, Budapest, Hungary. Theis, F., Lang, E., and Puntonet, C. (2004d). A geometric algorithm for overcomplete l<strong>in</strong>ear ICA. Neurocomput<strong>in</strong>g, 56:381–398. Theis, F., Meyer-Bäse, A., and Lang, E. (2004e). Second-order bl<strong>in</strong>d source separation based on multi-dimensional autocovariances. In Proc. ICA 2004, volume 3195 of LNCS, pages 726–733, Granada, Spa<strong>in</strong>. Spr<strong>in</strong>ger. Theis, F. and Nakamura, W. (2004). Quadratic <strong>in</strong>dependent component analysis. IEICE Trans. Fundamentals, E87-A(9):2355–2363. Theis, F., Puntonet, C., and Lang, E. (2006). Median-based cluster<strong>in</strong>g for underdeterm<strong>in</strong>ed bl<strong>in</strong>d signal process<strong>in</strong>g. IEEE Signal Process<strong>in</strong>g Letters, 13(2):96–99. Theis, F., Stadlthanner, K., and Tanaka, T. (2005c). First results on uniqueness of sparse non-negative matrix factorization. In Proc. EUSIPCO 2005, Antalya, Turkey. Theis, F. and Tanaka, T. (2005). A fast and efficient method for compress<strong>in</strong>g fMRI data sets. In Proc. ICANN 2005, volume 3697 of LNCS, pages 769–777, Warsaw, Poland. Spr<strong>in</strong>ger. Theis, F. and Tanaka, T. (2006). Sparseness by iterative projections onto spheres. In Proc. ICASSP 2006, Toulouse, France. Tomé, A. M., Teixeira, A. R., Lang, E. W., Stadlthanner, K., Rocha, A. P., and ALmeida, R. (2005). dAMUSE - A new tool for denois<strong>in</strong>g and BSS. Digital Signal Process<strong>in</strong>g.
BIBLIOGRAPHY 307 Tong, L., Liu, R.-W., Soon, V., and Huang, Y.-F. (1991). Indeterm<strong>in</strong>acy and identifiability of bl<strong>in</strong>d identification. IEEE Transactions on Circuits and Systems, 38:499–509. van der Veen, A. and Paulraj, A. (1996). An analytical constant modulus algorithm. IEEE Trans. Signal Process<strong>in</strong>g, 44(5):1–19. Vetter, R., Ves<strong>in</strong>, J. M., Celka, P., Renevey, P., and Krauss, J. (2002). Automatic nonl<strong>in</strong>ear noise reduction us<strong>in</strong>g local pr<strong>in</strong>cipal component analysis and MDL parameter selection. Proc. SPPRA 2002, pages 290–294. Vollgraf, R. and Obermayer, K. (2001). Multi-dimensional ICA to separate correlated sources. In Proc. NIPS 2001, pages 993–1000. Yeredor, A. (2000). Bl<strong>in</strong>d source separation via the second characteristic function. Signal Process<strong>in</strong>g, 80(5):897–902. Yeredor, A. (2002). Non-orthogonal jo<strong>in</strong>t diagonalization <strong>in</strong> the leastsquares sense with application <strong>in</strong> bl<strong>in</strong>d source separation. IEEE Trans. Signal Process<strong>in</strong>g, 50(7):1545–1553. Youla, D. and Webb, H. (1982). Image restoration by the methods of convex projections. part I — theory. IEEE Trans. Med. Imag<strong>in</strong>g, MI(I):81–94. Zibulevsky, M. and Pearlmutter, B. (2001). Bl<strong>in</strong>d source separation by sparse decomposition <strong>in</strong> a signal dictionary. Neural Computation, 13(4):863–882. Ziehe, A., Kawanabe, M., Harmel<strong>in</strong>g, S., and Müller, K.-R. (2003a). Bl<strong>in</strong>d separation of postnonl<strong>in</strong>ear mixtures us<strong>in</strong>g l<strong>in</strong>eariz<strong>in</strong>g transformations and temporal decorrelation. Journal of Mach<strong>in</strong>e Learn<strong>in</strong>g Research, 4:1319–1338. Ziehe, A., Laskov, P., Mueller, K.-R., and Nolte, G. (2003b). A l<strong>in</strong>ear least-squares algorithm for jo<strong>in</strong>t diagonalization. In Proc. of ICA 2003, pages 469–474, Nara, Japan. Ziehe, A. and Mueller, K.-R. (1998). TDSEP – an efficient algorithm for bl<strong>in</strong>d separation us<strong>in</strong>g time structure. In Niklasson, L., Bodén, M., and Ziemke, T., editors, Proc. of ICANN’98, pages 675–680, Skövde, Sweden. Spr<strong>in</strong>ger Verlag, Berl<strong>in</strong>.
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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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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 29 R 3 R 3 A BSRA R
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1.4. Sparseness 31 Sparse projectio
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58 Chapter 2. Neural Computation 16
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60 Chapter 2. Neural Computation 16
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62 Chapter 2. Neural Computation 16
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82 Chapter 3. Signal Processing 84(
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98 Chapter 4. Neurocomputing 64:223
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104 Chapter 5. IEICE TF E87-A(9):23
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124 Chapter 7. Proc. ISCAS 2005, pa
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126 Chapter 7. Proc. ISCAS 2005, pa
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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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132 Chapter 8. Proc. NIPS 2006 1.3
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134 Chapter 8. Proc. NIPS 2006 stru
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136 Chapter 8. Proc. NIPS 2006 5.5
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138 Chapter 8. Proc. NIPS 2006
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160 Chapter 10. IEEE TNN 16(4):992-
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162 Chapter 11. EURASIP JASP, 2007
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168 Chapter 11. EURASIP JASP, 2007
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170 Chapter 11. EURASIP JASP, 2007
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174 Chapter 11. EURASIP JASP, 2007
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176 Chapter 12. LNCS 3195:718-725,
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178 Chapter 12. LNCS 3195:718-725,
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180 Chapter 12. LNCS 3195:718-725,
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182 Chapter 12. LNCS 3195:718-725,
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184 Chapter 12. LNCS 3195:718-725,
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186 Chapter 13. Proc. EUSIPCO 2005
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188 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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194 Chapter 14. Proc. ICASSP 2006 A
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196 Chapter 14. Proc. ICASSP 2006
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198 Chapter 15. Neurocomputing, 69:
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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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234 Chapter 16. Proc. ICA 2006, pag
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236 Chapter 16. Proc. ICA 2006, pag
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238 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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252 Chapter 19. LNCS 3195:977-984,
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254 Chapter 19. LNCS 3195:977-984,
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- Page 306 and 307: 298 BIBLIOGRAPHY Barber, C., Dobkin
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