Mathematics in Independent Component Analysis

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228 Chapter 15. Neurocomputing, 69:1485-1501, 2006 [33] Ana Maria Tomé, Ana Rita Teixeira, Elmar Wolfgang Lang, Kurt Stadlthanner, A. P. Rocha, and R. ALmeida. dAMUSE - A new tool for denoising and BSS. Digital Signal Processing, 2005. [34] Ana Maria Tomé, Ana Rita Teixeira, Elmar Wolfgang Lang, Kurt Stadlthanner, and A. P. Rocha. Blind source separation using time-delayed signals. In International Joint Conference on Neural Networks, IJCNN’2004, volume CD, Budapest, Hungary, 2004. [35] Ana Maria Tomé. Blind source separation using a matrix pencil. In Int. Joint Conf. on Neural Networks, IJCNN’2000, Como, Italy, 2000. [36] Ana Maria Tomé. An iterative eigendecomposition approach to blind source separation. In 3rd Intern. Conf. on Independent Component Analysis and Signal Separation, ICA’2003, pages 424–428, San Diego, USA, 2001. [37] Lang Tong, Ruey wen Liu, Victor C. Soon, and Yih-Fang Huang. Indeterminacy and identifiability of blind identification. IEEE Transactions on Circuits and Systems, 38(5):499–509, 1991. [38] Rolf Vetter, J. M. Vesin, Patrick Celka, Philippe Renevey, and Jens Krauss. Automatic nonlinear noise reduction using local principal component analysis and MDL parameter selection. Proceedings of the IASTED International Conference on Signal Processing Pattern Recognition and Applications (SPPRA 02) Crete, pages 290–294, 2002. [39] Rolf Vetter. Extraction of efficient and characteristic features of multidimensional time series. PhD thesis, EPFL, Lausanne, 1999. [40] P. Vitányi and M. Li. Miminum description length induction, bayesianism, and kolmogorov complexity. IEEE Transactions on Information Theory, 46:446– 464, 2000. 31
Chapter 16 Proc. ICA 2006, pages 917-925 Paper F.J. Theis and M. Kawanabe. Uniqueness of non-gaussian subspace analysis. In Proc. ICA 2006, pages 917-925, Charleston, USA, 2006 Reference (Theis and Kawanabe, 2006) Summary in section 1.5.2 229
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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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1.5. Machine learning for data prep
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1.6. Applications to biomedical dat
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1.7. Outlook 51 noise data signal i
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Part II Papers 53
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56 Chapter 2. Neural Computation 16
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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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