Independent component analysis: theory and applications
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boston [u.a.]
Kluwer Acad. Publ.
1998
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXXIII, 210 S. graph. Darst. |
| ISBN: | 9781441950567 0792382617 |
Internformat
MARC
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| 100 | 1 | |a Lee, Te-Won |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Independent component analysis |b theory and applications |c by Te-Won Lee |
| 246 | 1 | 3 | |a ICA |
| 264 | 1 | |a Boston [u.a.] |b Kluwer Acad. Publ. |c 1998 | |
| 300 | |a XXXIII, 210 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Gaussian processes | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Signal processing |x Digital techniques | |
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| 689 | 0 | 1 | |a Statistische Analyse |0 (DE-588)4116599-8 |D s |
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Datensatz im Suchindex
| _version_ | 1804127205125521408 |
|---|---|
| adam_text | Contents
Abstract
XI
Preface
xiii
Acknowledgments
XVII
List of Figures
ХІХ
List of Tables
ХХІІІ
Abbreviations and Symbols
XXV
Introduction
ХХІХ
Part I Independent Component Analysis: Theory
1.
BASICS
5
1.1
Overview
5
1.2
Bayesian Probability Theory
6
1.3
Information Theory
7
1.3.1
Differential Entropy
10
1.3.2
Maximum Entropy
11
1.4
Artificial Neural Networks
13
1.4.1
Neural networks using unsuperv ised learning rules
14
1.4.2
The Principle of Maximum Entropy Preservation
18
1.5
Higher-Order Statistics
21
1.5.1
Moments
21
1.5.2
Cumulants
23
1.5.3
Cross-cumulants
23
1.6
Summary
24
2.
INDEPENDENT COMPONENT ANALYSIS
27
2.1
Overview
27
2.2
Problem statement and assumptions
29
2.3
The Poverty of PCA
31
vii
viii
ICA
THEORY AND APPLICATIONS
2.4
The Information Maximization Approach to
ICA
35
2.5
Derivation of the Infomax Learning Rule for
ICA
37
2.6
A simple but general
ICA
learning rule
42
2.6.1
Deriving the extended infomax learning rule to separate sub- and super-
Gaussian sources
43
2.6.2
Switching between nonlinearities
47
2.6.3
The hyperbolic-Cauchy density model
47
2.7
Simulations
49
2.7.1 10
Mixed Sound Sources
49
2.7.2 20
Mixed Sound Sources
52
2.8
Convergence properties in blind source separation
56
2.8.1
An intuition for the natural gradient
56
2.8.2
Robustness to parameter mismatch
58
2.9
Discussions
62
2.9.1
Comparison to other algorithms and architectures
62
2.9.2
Applications to real world problems
62
2.9.3
Biological plausibility
63
2.9.4
Limitations and future research
63
2.9.5
Conclusions
64
3.
A UNIFYING INFORMATION-THEORETIC FRAMEWORK FOR 1CA
67
3.1
Overview
67
3.2
Information Maximization
68
3.3
Negentropy Maximization
69
3.4
Maximum Likelihood Estimation
72
3.5
Higher-order moments and
cumulants
73
3.6
Nonlinear PCA
76
3.7 Bussgang
Algorithms
78
3.8
Conclusion
80
4.
BLIND SEPARATION OF TIME-DELAYED AND CONVOLVED SOURCES
83
4.1
Overview
83
4.2
Problem statement and assumptions
85
4.3
Feedback Architecture
86
4.3.1
Learning Rules
87
4.3.2
Simulations
89
4.4
Feedforward Architecture
90
4.4.1
Learning Rules
92
4.4.2
Simulations
94
4.5
Experiments in real environments
94
4.5.1
Speech Recognition Results
101
4.6 Bussgang
algorithms
101
4.7
Time-delayed decorrelation methods
102
4.7.1
Experimental Results with TDD
103
4.7.2
Discussions on TDD
105
4.8
Spectrogram
ICA
105
Contents
ЇХ
4.9
Conclusions
Юб
4.9.1
Future Research
107
5.
ICA
USING OVERCOMPLETE
REPRESENTATIONS
111
5.1
Learning Overcomplete Representations
113
5.1.1
Inferring the
sources s
113
5.1.2
Learning the basis vectors A
113
5.2
Experimental Results
114
5.2.1
Blind Separation of Speech Signals
114
5.2.2
Blind Separation of Speech and Music Signals
116
5.2.3
Preliminary results with other mixtures
117
5.3
Discussion
117
5.3.1
Comparison to other methods
117
5.3.2
Conclusions
119
6.
FIRST STEPS TOWARDS NONLINEAR
ICA
123
6.1
Overview
123
6.2
A simple nonlinear mixing model
124
6.3
Learning Algorithm
125
6.3.1
Learning Rules for Sigmoidal Nonlinear Mixing
126
6.3.2
Learning Rules for Flexible Nonlinearities
127
6.4
Simulation Results
128
6.5
A linearization approach to nonlinear
ICA
130
6.6
Discussions
135
6.6.1
Other approaches to nonlinear
ICA
135
6.6.2
Conclusions and future research
137
Part II Independent Component Analysis: Applications
7.
BIOMEDICAL
APPLICATIONS OF
ICA
145
7.1
Overview
145
7.2
ICA
of Electroencephalographic Data
147
7.2.1
Simple examples of applying
ICA
to
EEG
data
148
7.3
EEG
artifact removal using extended infomax
149
7.3.1
Methods and Materials
155
7.3.2
Discussion of
EEG
applications
157
7.4
Functional Magnetic Resonance Imaging Analysis
157
7.4.1
fMRI Methods
158
7.4.2
fMRI Results
159
7.4.3
Discussions and Conclusions on fMRI
160
7.5
Conclusions and future research
163
8.
ICA
FOR FEATURE EXTRACTION
167
8.1
Overview
167
8.2
ICA
of natural images
168
8.3
ICA
of natural ¡mages using extended infomax
169
X
ICA
THEORY AND APPLICATIONS
8.4
ICA
for Feature Extraction
170
8.5
Discussions and Conclusions
173
9.
UNSUPERVISED CLASSIFICATION WITH
ICA
MIXTURE MODELS
177
9.1
Overview
177
9.2
The
ICA
Mixture Model
178
9.3
Simulations
181
9.4
Iris Data Classification
181
9.5
Conclusions
182
10.
CONCLUSIONS AND FUTURE RESEARCH
187
10.1
Conclusions
187
10.2
Future Research
188
Bibliography
· 193
About the author
207
Index
209
|
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| spelling | Lee, Te-Won Verfasser aut Independent component analysis theory and applications by Te-Won Lee ICA Boston [u.a.] Kluwer Acad. Publ. 1998 XXXIII, 210 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Gaussian processes Neural networks (Computer science) Signal processing Digital techniques Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf 1\p (DE-588)4113937-9 Hochschulschrift gnd-content Signalverarbeitung (DE-588)4054947-1 s Statistische Analyse (DE-588)4116599-8 s DE-604 Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008527114&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Lee, Te-Won Independent component analysis theory and applications Gaussian processes Neural networks (Computer science) Signal processing Digital techniques Statistische Analyse (DE-588)4116599-8 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| subject_GND | (DE-588)4116599-8 (DE-588)4054947-1 (DE-588)4113937-9 |
| title | Independent component analysis theory and applications |
| title_alt | ICA |
| title_auth | Independent component analysis theory and applications |
| title_exact_search | Independent component analysis theory and applications |
| title_full | Independent component analysis theory and applications by Te-Won Lee |
| title_fullStr | Independent component analysis theory and applications by Te-Won Lee |
| title_full_unstemmed | Independent component analysis theory and applications by Te-Won Lee |
| title_short | Independent component analysis |
| title_sort | independent component analysis theory and applications |
| title_sub | theory and applications |
| topic | Gaussian processes Neural networks (Computer science) Signal processing Digital techniques Statistische Analyse (DE-588)4116599-8 gnd Signalverarbeitung (DE-588)4054947-1 gnd |
| topic_facet | Gaussian processes Neural networks (Computer science) Signal processing Digital techniques Statistische Analyse Signalverarbeitung Hochschulschrift |
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