Nonlinear Source Separation

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Morgan & Claypool Publishers, 2006 - Technology & Engineering - 99 pages
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Nonlinear source separation is the subject of much recent study, although until now no complete overview has been available. Author Luis Almeida fills this gap by providing a comprehensive look at the state of the art for practicing scientists and graduate students. Foundations and a detailed overview for nonlinear source separation methods are provided, with thorough references for readers who need more information on implementation of a specific method.Readers are assumed to have a basic knowledge of nonlinear trainable systems, including multilayer perceptrons (MLPs), and also the backpropagationtraining method and gradient-based optimization.Key Features:* A detailed introduction to nonlinear source separation* A discussion of the post-nonlinear setting, with theoretical results guaranteeing source recovery* A review of unconstrained separation methods, including MISEP, ensemble learning, and kTDSEP
 

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Contents

Introduction
1
11 BASIC CONCEPTS
2
111 The Scatter Plot as a Tool To Depict Joint Distributions
3
112 Separation Criteria
5
12 SUMMARY
6
Linear Source Separation
7
22 INFOMAX
8
221 Choice of the Output Nonlinearities
11
Nonlinear Separation
31
31 POSTNONLINEAR MIXTURES
33
311 Separation Method
35
312 Application Example
37
32 UNCONSTRAINED NONLINEAR SEPARATION
40
322 Nonlinear ICA Through Ensemble Learning
51
kTDSEP
66
324 Other Methods
73

222 Maximum Entropy Estimation of the Nonlinearities
12
223 Estimation of the Score Functions
21
23 EXPLOITING THE TIMED OMAIN STRUCTURE
24
231 An Example
26
JADE AND FASTICA
27
241 JADE
28
242 FastICA
29
33 CONCLUSION
77
Final Comments
79
Statistical Concepts
83
A21 Entropy of a Transformed Variable
84
A3 KULLBACKLEIBLER DIVERGENCE
85
Online Software and Data
87
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