Mathematics in Independent Component Analysis

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196 Chapter 14. Proc. ICASSP 2006
Chapter 15 Neurocomputing, 69:1485-1501, 2006 Paper P.Gruber, K.Stadlthanner, M.Böhm, F.J. Theis, E.W. Lang, A.M. Tomé, A.R. Teixeira, C.G. Puntonet, and J.M.Górriz Saéz. Denoising using local projective subspace methods. Neurocomputing, 69:1485-1501, 2006 Reference (Gruber et al., 2006) Summary in section 1.5.2 197
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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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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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306 BIBLIOGRAPHY Theis, F. and Inou
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