Handbook of blind source separation: independent component analysis and applications
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Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo
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| Beschreibung: | XXIV, 831 Seiten 25 cm |
| ISBN: | 9780123747266 0123747260 |
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| 245 | 1 | 0 | |a Handbook of blind source separation |b independent component analysis and applications |c edited by P. Comon and C. Jutten |
| 250 | |a First edition | ||
| 264 | 1 | |a Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo |b Elsevier |c 2010 | |
| 300 | |a XXIV, 831 Seiten |c 25 cm | ||
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| 650 | 4 | |a Blind source separation | |
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HANDBOOK OF BLIND SOURCE SEPARATION INDEPENDENT COMPONENT ANALYSIS AND
APPLICATIONS EDITED BY P. COMON AND * JUTTEN AMSTERDAM * BOSTON *
HEIDELBERG * LONDON * NEW YORK * OXFORD PARIS * SAN DIEGO * SAN
FRANCISCO * SINGAPORE * SYDNEY * TOKYO ACADEMIC PRESS IS AN IMPRINT OF
ELSEVIER CONTENTS ABOUT THE EDITORS XIX PREFACE XXI CONTRIBUTORS XXIII
CHAPTER 1 INTRODUCTION 1 1.1 GENESIS OF BLIND SOURCE SEPARATION 1 1.1.1
A BIOLOGICAL PROBLEM 3 1.1.2 CONTEXTUAL DIFFICULTIES 6 1.1.3 A FEW
HISTORICAL NOTES 7 1.2 PROBLEM FORMALIZATION 10 1.2.1 INVERTIBLE
MIXTURES 11 1.2.2 UNDERDETERMINED MIXTURES 11 1.3 SOURCE SEPARATION
METHODS 11 1.3.1 INDEPENDENT COMPONENT ANALYSIS 12 1.3.2 NON-TEMPORALLY
IID SOURCES 12 1.3.3 OTHER APPROACHES 13 1.4 SPATIAL WHITENING, NOISE
REDUCTION AND PCA 13 1.5 APPLICATIONS 15 1.6 CONTENT OF THE HANDBOOK 15
REFERENCES 19 CHAPTER 2 INFORMATION 23 2.1 INTRODUCTION 23 2.2 METHODS
BASED ON MUTUAL INFORMATION 24 2.2.1 MUTUAL INFORMATION BETWEEN RANDOM
VECTORS 24 2.2.2 THE MIXING MODEL AND SEPARATION CRITERION 25 2.2.3
EMPIRICAL CRITERIA AND ENTROPY ESTIMATORS 26 2.2.4 COMPUTATION OF
ENTROPY ESTIMATORS 29 2.2.5 MINIMIZATION OF THE EMPIRICAL CRITERIA 32
2.2.6 STATISTICAL PERFORMANCE 39 2.3 METHODS BASED ON MUTUAL INFORMATION
RATE 45 2.3.1 ENTROPY AND MUTUAL INFORMATION RATE 45 2.3.2 CONTRASTS 48
2.3.3 ESTIMATION AND SEPARATION 53 V VI CONTENTS 2.4 CONCLUSION AND
PERSPECTIVES 61 REFERENCES 62 CHAPTER 3 CONTRASTS 65 3.1 INTRODUCTION 65
3.1.1 MODEL AND NOTATION 66 3.1.2 PRINCIPLE OF CONTRAST FUNCTIONS 66
3.1.3 BIBLIOGRAPHICAL REMARKS 67 3.2 CUMULANTS 67 3.3 MISO CONTRASTS 69
3.3.1 MISO CONTRASTS FOR STATIC MIXTURES 69 3.3.2 DEFLATION PRINCIPLE 71
3.3.3 MISO CONTRASTS FOR CONVOLUTIVE MIXTURES 72 3.4 MIMO CONTRASTS FOR
STATIC MIXTURES 78 3.4.1 INTRODUCTION 78 3.4.2 CONTRASTS FOR MIMO STATIC
MIXTURES 79 3.4.3 CONTRASTS AND JOINT DIAGONALIZATION 87 3.4.4
NON-SYMMETRIC CONTRASTS 89 3.4.5 CONTRASTS WITH REFERENCE SIGNALS 90 3.5
MIMO CONTRASTS FOR DYNAMIC MIXTURES 92 3.5.1 SPACE-TIME WHITENING 92
3.5.2 CONTRASTS FOR MIMO CONVOLUTIVE MIXTURES 93 3.5.3 CONTRASTS AND
JOINT DIAGONALIZATION 95 3.5.4 NON-SYMMETRIC CONTRASTS 99 3.6
CONSTRUCTING OTHER CONTRAST CRITERIA 101 3.7 CONCLUSION 102 REFERENCES
103 CHAPTER 4 LIKELIHOOD 107 4.1 INTRODUCTION: MODELS AND LIKELIHOOD 107
4.2 TRANSFORMATION MODEL AND EQUIVARIANCE 109 4.2.1 TRANSFORMATION
LIKELIHOOD 110 4.2.2 TRANSFORMATION CONTRAST ILL 4.2.3 RELATIVE
VARIATIONS ILL 4.2.4 THE IID GAUSSIAN MODEL AND DECORRELATION 113 4.2.5
EQUIVARIANT ESTIMATORS AND UNIFORM PERFORMANCE 115 4.2.6 SUMMARY 116 4.3
INDEPENDENCE 116 4.3.1 SCORE FUNCTION AND ESTIMATING EQUATIONS 117 4.3.2
MUTUAL INFORMATION 118 4.3.3 MUTUAL INFORMATION, CORRELATION AND 119
4.3.4 SUMMARY 121 CONTENTS VII 4.4 IDENTIFIABILITY, STABILITY,
PERFORMANCE 122 4.4.1 ELEMENTS OF ASYMPTOTIC ANALYSIS 123 4.4.2 FISHER
INFORMATION MATRIX 125 4.4.3 BLIND IDENTIFIABILITY 126 4.4.4 ASYMPTOTIC
PERFORMANCE 126 4.4.5 WHEN THE SOURCE MODEL IS WRONG 127 4.4.6 RELATIVE
GRADIENT AND NATURAL GRADIENT 130 4.4.7 SUMMARY 130 4.5 NON-GAUSSIAN
MODELS 131 4.5.1 THE LIKELIHOOD CONTRAST IN IID MODELS 131 4.5.2 SCORE
FUNCTIONS AND ESTIMATING EQUATIONS 132 4.5.3 GAUSSIANITY INDEX 132 4.5.4
CRAMER-RAO BOUND 133 4.5.5 ASYMPTOTIC PERFORMANCE 133 4.5.6 ADAPTIVE
SCORES 134 4.5.7 ALGORITHMS 135 4.6 GAUSSIAN MODELS 136 4.6.1 DIAGONAL
GAUSSIAN MODELS 136 4.6.2 FISHER INFORMATION AND DIVERSITY 137 4.6.3
GAUSSIAN CONTRASTS 138 4.6.4 IN PRACTICE: LOCALIZED GAUSSIAN MODELS 140
4.6.5 CHOOSING THE DIAGONALIZING TRANSFORM 141 4.7 NOISY MODELS 142
4.7.1 SOURCE ESTIMATION FROM NOISY MIXTURES 142 4.7.2 NOISY LIKELIHOOD
FOR LOCALIZED GAUSSIAN MODELS 143 4.7.3 NOISY LIKELIHOOD AND HIDDEN
VARIABLES 144 4.8 CONCLUSION: A GENERAL VIEW 148 4.8.1 UNITY 148 4.8.2
DIVERSITY 149 4.9 APPENDIX: PROOFS 152 REFERENCES 153 CHAPTER 5
ALGEBRAIC METHODS AFTER PREWHITENING 155 5.1 INTRODUCTION 155 5.1.1
MULTILINEAR ALGEBRA 155 5.1.2 HIGHER-ORDER STATISTICS 156 5.1.3 JACOBI
ITERATION 159 5.2 INDEPENDENT COMPONENT ANALYSIS 161 5.2.1 ALGEBRAIC
FORMULATION 161 5.2.2 STEP 1: PREWHITENING 163 5.2.3 STEP 2: FIXING THE
ROTATIONAL DEGREES OF FREEDOM USING THE HIGHER-ORDER CUMULANT 164 VIII
CONTENTS 5.3 DIAGONALIZATION IN LEAST SQUARES SENSE 165 5.3.1
THIRD-ORDER REAL CASE 167 5.3.2 THIRD-ORDER COMPLEX CASE 168 5.3.3
FOURTH-ORDER REAL CASE 169 5.3.4 FOURTH-ORDER COMPLEX CASE 170 5.4
SIMULTANEOUS DIAGONALIZATION OF MATRIX SLICES 170 5.4.1 REAL CASE 173
5.4.2 COMPLEX CASE 173 5.5 SIMULTANEOUS DIAGONALIZATION OF THIRD-ORDER
TENSOR SLICES 174 5.6 MAXIMIZATION OF THE TENSOR TRACE 174 REFERENCES
175 CHAPTER 6 ITERATIVE ALGORITHMS 179 6.1 INTRODUCTION 179 6.2
MODELANDGOAL 180 6.3 CONTRAST FUNCTIONS FOR ITERATIVE BSS/ICA 181 6.3.1
INFORMATION-THEORETIC CONTRASTS 181 6.3.2 CUMULANT-BASED APPROXIMATIONS
182 6.3.3 CONTRASTS FOR SOURCE EXTRACTION 184 6.3.4 NONLINEAR FUNCTION
APPROXIMATIONS 185 6.4 ITERATIVE SEARCH ALGORITHMS: GENERALITIES 186
6.4.1 BATCH METHODS 186 6.4.2 STOCHASTIC OPTIMIZATION 189 6.4.3 BATCH OR
ADAPTIVE ESTIMATES? 191 6.5 ITERATIVE WHITENING 192 6.6 CLASSICAL
ADAPTIVE ALGORITHMS 193 6.6.1 HERAULT-JUTTEN ALGORITHM 193 6.6.2
SELF-NORMALIZED NETWORKS 194 6.6.3 ADAPTIVE ALGORITHMS BASED ON
CONTRASTS 195 6.6.4 ADAPTIVE ALGORITHMS BASED ON CENTROIDS 198 6.7
RELATIVE (NATURAL) GRADIENT TECHNIQUES 199 6.7.1 RELATIVE GRADIENT AND
SERIAL UPDATING 199 6.7.2 ADAPTIVE ALGORITHMS BASED ON THE RELATIVE
GRADIENT 201 6.7.3 LIKELIHOOD MAXIMIZATION WITH THE RELATIVE GRADIENT
202 6.8 ADAPTING THE NONLINEARITIES 203 6.9 ITERATIVE ALGORITHMS BASED
ON DEFLATION 205 6.9.1 ADAPTIVE DEFLATION ALGORITHM BY DELFOSSE AND
LOUBATON . 205 6.9.2 REGRESSION-BASED DEFLATION 207 6.9.3 DEFLATIONARY
ORTHOGONALIZATION 207 CONTENTS IX 6.10 THE FASTICA ALGORITHM 208 6.10.1
INTRODUCTION 208 6.10.2 IMPLICIT ADAPTATION OF THE CONTRAST IN FASTICA
209 6.10.3 DERIVATION OF FASTICA AS A NEWTON ITERATION 209 6.10.4
CONNECTION WITH GRADIENT METHODS 211 6.10.5 CONVERGENCE OF FASTICA 212
6.10.6 FASTICA USING CUMULANTS 213 6.10.7 VARIANTS OF FASTICA 214 6.1 1
ITERATIVE ALGORITHMS WITH OPTIMAL STEP SIZE 216 6.11.1 OPTIMIZING THE
STEP SIZE 216 6.11.2 THE ROBUSTICA ALGORITHM 217 6.12 SUMMARY,
CONCLUSIONS AND OUTLOOK 220 REFERENCES 221 CHAPTER 7 SECOND-ORDER
METHODS BASED ON COLOR 227 7.1 INTRODUCTION 227 7.2 WSS PROCESSES 228
7.2.1 PARAMETRIC WSS PROCESSES 230 7.3 PROBLEM FORMULATION,
IDENTIFIABILITY AND BOUNDS 232 7.3.1 INDETERMINACIES AND IDENTIFIABILITY
233 7.3.2 PERFORMANCE MEASURES AND BOUNDS 237 7.4 SEPARATION BASED ON
JOINT DIAGONALIZATION 245 7.4.1 ON EXACT AND APPROXIMATE JOINT
DIAGONALIZATION 246 7.4.2 THE FIRST JD-BASED METHOD 251 7.4.3 AMUSE AND
ITS MODIFIED VERSIONS 251 7.4.4 SOBI, TDSEP AND MODIFIED VERSIONS 253
7.5 SEPARATION BASED ON MAXIMUM LIKELIHOOD 260 7.5.1 THE QML APPROACH
261 7.5.2 THE EML APPROACH 266 7.5.3 THE GMI APPROACH 268 7.6 ADDITIONAL
ISSUES 270 7.6.1 THE EFFECT OF ADDITIVE NOISE 270 7.6.2 NON-STATIONARY
SOURCES, TIME-VARYING MIXTURES 273 7.6.3 COMPLEX-VALUED SOURCES 275
REFERENCES 276 CHAPTER 8 CONVOLUTIVE MIXTURES 281 8.1 INTRODUCTION AND
MIXTURE MODEL 281 8.1.1 MODEL AND NOTATIONS 281 8.1.2 CHAPTER
ORGANIZATION 282 X CONTENTS 8.2 INVERTIBILITY OF CONVOLUTIVE MIMO
MIXTURES 283 8.2.1 GENERAL RESULTS 284 8.2.2 FIR SYSTEMS AND POLYNOMIAL
MATRICES 285 8.3 ASSUMPTIONS 287 8.3.1 FUNDAMENTAL ASSUMPTIONS 287 8.3.2
INDETERMINACIES 288 8.3.3 LINEAR AND NONLINEAR SOURCES 289 8.3.4
SEPARATION CONDITION 291 8.4 JOINT SEPARATING METHODS 292 8.4.1
WHITENING 292 8.4.2 TIME DOMAIN APPROACHES 294 8.4.3 FREQUENCY DOMAIN
APPROACHES 298 8.5 ITERATIVE AND DEFLATION METHODS 301 8.5.1 EXTRACTION
OF ONE SOURCE 301 8.5.2 DEFLATION 308 8.6 NON-STATIONARY CONTEXT 309
8.6.1 CONTEXT 309 8.6.2 SOME PROPERTIES OF CYCLOSTATIONARY TIME-SERIES
310 8.6.3 DIRECT EXTENSION OF THE RESULTS OF SECTION 8.5 FOR THE SOURCE
EXTRACTION, AND WHY IT IS DIFFICULT TO IMPLEMENT 314 8.6.4 A FUNCTION TO
MINIMIZE: A CONTRAST? 318 REFERENCES 322 CHAPTER 9 ALGEBRAIC
IDENTIFICATION OF UNDER-DETERMINED MIXTURES 325 9.1 OBSERVATION MODEL
325 9.2 INTRINSIC IDENTIFIABILITY 326 9.2.1 EQUIVALENT REPRESENTATIONS
326 9.2.2 MAIN THEOREM 327 9.2.3 CORE EQUATION 329 9.2.4 IDENTIFIABILITY
IN THE 2-DIMENSIONAL CASE 330 9.3 PROBLEM FORMULATION 332 9.3.1 APPROACH
BASED ON DERIVATIVES OF THE JOINT CHARACTERISTIC FUNCTION 332 9.3.2
APPROACH BASED ON CUMULANTS 333 9.4 HIGHER-ORDER TENSORS 337 9.4.1
CANONICAL TENSOR DECOMPOSITION 338 9.4.2 ESSENTIAL UNIQUENESS 340 9.4.3
COMPUTATION 344 9.5 TENSOR-BASED ALGORITHMS 345 9.5.1 VECTOR AND MATRIX
REPRESENTATIONS 345 9.5.2 THE 2-DIMENSIONAL CASE 346 CONTENTS XI 9.5.3
SOBIUM FAMILY 350 9.5.4 FOOBIFAMILY 354 9.5.5 BIOME FAMILY 357 9.5.6
ALESCAF AND LEMACAF 359 9.5.7 OTHER ALGORITHMS 360 9.6 APPENDIX:
EXPRESSIONS OF COMPLEX CUMULANTS 360 REFERENCES 362 CHAPTER 10 SPARSE
COMPONENT ANALYSIS 367 10.1 INTRODUCTION 367 10.2 SPARSE SIGNAL
REPRESENTATIONS 370 10.2.1 BASIC PRINCIPLES OF SPARSITY 371 10.2.2
DICTIONARIES 372 10.2.3 LINEAR TRANSFORMS 373 10.2.4 ADAPTIVE
REPRESENTATIONS 374 10.3 JOINT SPARSE REPRESENTATION OF MIXTURES 374
10.3.1 PRINCIPLE 375 10.3.2 LINEAR TRANSFORMS 375 10.3.3 PRINCIPLE OF T
T MINIMIZATION 377 10.3.4 BAYESIAN INTERPRETATION OF I R CRITERIA 377
10.3.5 EFFECT OF THE CHOSEN I T CRITERION 379 10.3.6 OPTIMIZATION
ALGORITHMS FOR I T CRITERIA 381 10.3.7 MATCHING PURSUIT 385 10.3.8
SUMMARY 386 10.4 ESTIMATING THE MIXING MATRIX BY CLUSTERING 388 10.4.1
GLOBAL CLUSTERING ALGORITHMS 389 10.4.2 SCATTER PLOT SELECTION IN
MULTISCALE REPRESENTATIONS 391 10.4.3 USE OF LOCAL SCATTER PLOTS IN THE
TIME-FREQUENCY PLANE 395 10.5 SQUARE MIXING MATRIX: RELATIVE NEWTON
METHOD 396 10.5.1 RELATIVE OPTIMIZATION FRAMEWORK 397 10.5.2 NEWTON
METHOD 398 10.5.3 GRADIENT AND HESSIAN EVALUATION 399 10.5.4 SEQUENTIAL
OPTIMIZATION 400 10.5.5 NUMERICAL ILLUSTRATIONS 401 10.5.6 EXTENSION OF
RELATIVE NEWTON: BLIND DECONVOLUTION 402 10.6 SEPARATION WITH A KNOWN
MIXING MATRIX 403 10.6.1 LINEAR SEPARATION OF (OVER-)DETERMINED MIXTURES
404 10.6.2 BINARY MASKING ASSUMING A SINGLE ACTIVE SOURCE 405 10.6.3
BINARY MASKING ASSUMING M P ACTIVE SOURCES 405 10.6.4 LOCAL SEPARATION
BY T MINIMIZATION 406 10.6.5 PRINCIPLE OF GLOBAL SEPARATION BY I T
MINIMIZATION 407 10.6.6 FORMAL LINKS WITH SINGLE-CHANNEL TRADITIONAL
SPARSE APPROX- IMATION 408 10.6.7 GLOBAL SEPARATION ALGORITHMS USING T
MINIMIZATION 408 10.6.8 ITERATIVE GLOBAL SEPARATION: DEMIXING PURSUIT
409 10.7 CONCLUSION 410 10.8 OUTLOOK 412 REFERENCES 414 CHAPTER 11
QUADRATIC TIME-FREQUENCY DOMAIN METHODS 421 11.1 INTRODUCTION 421 11.2
PROBLEM STATEMENT 422 11.2.1 MODEL AND ASSUMPTIONS 422 11.2.2
INDETERMINACIES AND SOURCES ESTIMATION 422 11.2.3 SPATIAL WHITENING 423
11.2.4 A GENERALIZATION TO THE NOISY CASE 425 11.3 SPATIAL QUADRATIC T-F
SPECTRA AND REPRESENTATIONS 427 11.3.1 BILINEAR AND QUADRATIC TRANSFORMS
427 11.3.2 SPATIAL BILINEAR AND QUADRATIC TRANSFORMS 427 11.3.3
(SPATIAL) QUADRATIC TIME-FREQUENCY REPRESENTATIONS 428 11.3.4 (SPATIAL)
BILINEAR AND QUADRATIC TIME-FREQUENCY SPECTRA 430 11.3.5 DESCRIPTIONS OF
KEY PROPERTIES AND MODEL STRUCTURE; ADDI- TIONAL ASSUMPTIONS ABOUT THE
SOURCES 430 11.3.6 EXAMPLE 432 11.4 TIME-FREQUENCY POINTS SELECTION 435
11.4.1 AUTOMATIC TIME-FREQUENCY POINTS SELECTION IN A WHITENED CONTEXT
436 11.4.2 AUTOMATIC TIME-FREQUENCY POINTS SELECTION IN A NON-WHITENED
CONTEXT 438 11.5 SEPARATION ALGORITHMS 440 11.5.1 JOINT DIAGONALIZATION
AND/OR JOINT ZERO-DIAGONALIZATION CRITERIA 441 11.5.2 WHITENED-BASED
SEPARATION ALGORITHMS 443 11.5.3 NON-WHITENED BASED SEPARATION
ALGORITHMS 445 11.5.4 ALGEBRAIC METHODS AND CLASSIFICATION 449 11.6
PRACTICAL AND COMPUTER SIMULATIONS 452 11.6.1 SYNTHETIC SOURCE SIGNALS
452 11.6.2 MIXTURE 453 11.6.3 TIME-FREQUENCY POINTS SELECTION 453 11.6.4
RESULTS 455 11.7 SUMMARY AND CONCLUSION 462 REFERENCES 464 CONTENTS XIII
CHAPTER 12 BAYESIAN APPROACHES 467 12.1 INTRODUCTION 467 1 2.2 SOURCE
SEPARATION FORWARD MODEL AND NOTATIONS 468 1 2.3 GENERAL BAYESIAN SCHEME
470 12.4 RELATION TO PCA AND ICA 471 12.5 PRIOR AND LIKELIHOOD
ASSIGNMENTS 477 12.5.1 GENERAL ASSIGNMENTS 478 12.5.2 PHYSICAL PRIORS
479 12.6 SOURCE MODELING 482 12.6.1 MODELING STATIONARY WHITE SOURCES
482 12.6.2 ACCOUNTING FOR TEMPORAL CORRELATIONS OF THE SOURCES 486
12.6.3 MODELING NON-STATIONARY SOURCES 488 12.7 ESTIMATION SCHEMES 493
12.8 SOURCE SEPARATION APPLICATIONS 494 12.8.1 SPECTROMETRY 494 12.8.2
SOURCE SEPARATION IN ASTROPHYSICS 494 12.8.3 SOURCE SEPARATION IN
SATELLITE IMAGING 495 12.8.4 DATA REDUCTION, CLASSIFICATION AND
SEPARATION IN HYPERSPEC- TRAL IMAGING 497 12.9 SOURCE CHARACTERIZATION
499 12.9.1 SOURCE SEPARATION AND LOCALIZATION 499 12.9.2 NEURAL SOURCE
ESTIMATION 501 12.9.3 SOURCE CHARACTERIZATION IN BIOPHYSICS 504 12.10
CONCLUSION 508 REFERENCES 509 CHAPTER 13 NON-NEGATIVE MIXTURES 515 13.1
INTRODUCTION 515 13.2 NON-NEGATIVE MATRIX FACTORIZATION 515 13.2.1
SIMPLE GRADIENT DESCENT 517 13.2.2 MULTIPLICATIVE UPDATES 518 13.2.3
ALTERNATING LEAST SQUARES (ALS) 520 1 3.3 EXTENSIONS AND MODIFICATIONS
OF NMF 521 13.3.1 CONSTRAINTS AND PENALTIES 521 13.3.2 RELAXING THE
NON-NEGATIVITY CONSTRAINTS 525 13.3.3 STRUCTURAL FACTOR CONSTRAINTS 526
13.3.4 MULTI-FACTOR AND TENSOR MODELS 528 13.3.5 ALS ALGORITHMS FOR
NON-NEGATIVE TENSOR FACTORIZATION 532 13.4 FURTHER NON-NEGATIVE
ALGORITHMS 534 13.4.1 NEURAL NETWORK APPROACHES 534 13.4.2 GEOMETRICAL
METHODS 535 13.4.3 ALGORITHMS FOR LARGE-SCALE NMF PROBLEMS 537 XIV
CONTENTS 13.5 APPLICATIONS 539 13.5.1 AIR QUALITY AND CHEMOMETRICS 539
13.5.2 TEXT ANALYSIS 540 13.5.3 IMAGE PROCESSING 541 13.5.4 AUDIO
ANALYSIS 541 13.5.5 GENE EXPRESSION ANALYSIS 541 13.6 CONCLUSIONS 542
REFERENCES 542 CHAPTER 14 NONLINEAR MIXTURES 549 14.1 INTRODUCTION 549
14.2 NONLINEAR ICA IN THE GENERAL CASE 550 14.2.1 NONLINEAR INDEPENDENT
COMPONENT ANALYSIS (ICA) 550 14.2.2 DEFINITIONS AND PRELIMINARY RESULTS
550 14.2.3 EXISTENCE AND UNIQUENESS OF TRANSFORMS PRESERVING INDEPEN-
DENCE 551 14.3 ICA FOR CONSTRAINED NONLINEAR MIXTURES 554 14.3.1
STRUCTURAL CONSTRAINTS 554 14.3.2 SMOOTH TRANSFORMS 555 14.3.3 EXAMPLE
OF LINEAR MIXTURES 557 14.3.4 CONFORMAL MAPPINGS 557 14.3.5
POST-NONLINEAR (PNL) MIXTURES 558 14.3.6 BILINEAR MIXTURES 561 14.3.7 A
CLASS OF SEPARABLE NONLINEAR MAPPINGS 563 1 4.4 PRIOR S ON SOURCES 567
14.4.1 BOUNDED SOURCES IN PNL MIXTURES 567 14.4.2 TEMPORALLY CORRELATED
SOURCES IN NONLINEAR MIXTURES 568 14.5 INDEPENDENCE CRITERIA 570 14.5.1
MUTUAL INFORMATION 570 14.5.2 DIFFERENTIAL OF THE MUTUAL INFORMATION 573
14.5.3 QUADRATIC CRITERION 574 14.6 A BAYESIAN APPROACH FOR GENERAL
MIXTURES 575 14.6.1 THE NONLINEAR FACTOR ANALYSIS (NFA) METHOD 576
14.6.2 EXTENSIONS AND EXPERIMENTAL RESULTS 579 14.6.3 COMPARISONS ON PNL
MIXTURES 579 14.7 OTHER METHODS AND ALGORITHMS 580 14.7.1 ALGORITHMS FOR
PNL MIXTURES 580 14.7.2 CONSTRAINED MLP-LIKE STRUCTURES 580 14.7.3 OTHER
APPROACHES 581 14.8 A FEW APPLICATIONS 581 14.8.1 CHEMICAL SENSORS 582
CONTENTS XV 14.8.2 GAS SENSORS 582 14.8.3 SHOW-THROUGH REMOVAL 583 14.9
CONCLUSION 584 REFERENCES 586 CHAPTER 15 SEMI-BLIND METHODS FOR
COMMUNICATIONS 593 15.1 INTRODUCTION 593 15.1.1 BLIND SOURCE SEPARATION
AND CHANNEL EQUALIZATION 593 15.1.2 GOALS AND ORGANIZATION OF THE
CHAPTER 594 15.2 TRAINING-BASED AND BLIND EQUALIZATION 595 15.2.1
TRAINING-BASED OR SUPERVISED EQUALIZATION 595 15.2.2 BLIND EQUALIZATION
595 15.2.3 A CLASSICAL BLIND CRITERION: THE CONSTANT MODULUS 596 15.3
OVERCOMING THE LIMITATIONS OF BLIND METHODS 597 15.3.1 ALGEBRAIC
SOLUTIONS 597 15.3.2 MULTI-CHANNEL SYSTEMS 598 15.3.3 SEMI-BLIND
APPROACH 598 1 5.4 MATHEMATICAL FORMULATION 599 15.4.1 SIGNAL MODEL 599
15.4.2 NOTATIONS 600 1 5.5 CHANNEL EQUALIZATION CRITERIA 601 15.5.1
SUPERVISED, BLIND AND SEMI-BLIND CRITERIA 601 15.5.2 RELATIONSHIPS
BETWEEN EQUALIZATION CRITERIA 602 15.6 ALGEBRAIC EQUALIZERS 604 15.6.1
ALGEBRAIC MMSE EQUALIZER 604 15.6.2 ALGEBRAIC BLIND EQUALIZERS 605
15.6.3 ALGEBRAIC SEMI-BLIND EQUALIZERS 610 15.7 ITERATIVE EQUALIZERS 610
15.7.1 CONVENTIONAL GRADIENT-DESCENT ALGORITHMS 610 15.7.2 ALGORITHMS
BASED ON ALGEBRAIC OPTIMAL STEP SIZE 612 15.8 PERFORMANCE ANALYSIS 616
15.8.1 PERFORMANCE OF ALGEBRAIC BLIND EQUALIZERS 616 15.8.2 ATTRACTION
BASINS OF BLIND AND SEMI-BLIND CP EQUALIZERS 618 15.8.3 ROBUSTNESS OF
OPTIMAL STEP-SIZE CM EQUALIZERS TO LOCAL EXTREMA 619 15.8.4 CP
EQUALIZERS FOR A NON-MINIMUM PHASE CHANNEL 621 15.8.5 BLIND CM AND
SEMI-BLIND CM-MMSE EQUALIZERS 624 15.8.6 INFLUENCE OF PILOT-SEQUENCE
LENGTH 626 15.8.7 INFLUENCE OF THE RELATIVE WEIGHT BETWEEN BLIND AND
SUPER- VISED CRITERIA 627 15.8.8 COMPARISON BETWEEN THE CM AND CP
CRITERIA 627 15.9 SEMI-BLIND CHANNEL ESTIMATION 628 15.10 SUMMARY,
CONCLUSIONS AND OUTLOOK 632 REFERENCES 633 CHAPTER 16 OVERVIEW OF SOURCE
SEPARATION APPLICATIONS 639 16.1 INTRODUCTION 639 16.1.1 CONTEXT 639
16.1.2 HISTORICAL SURVEY 639 16.1.3 ORGANIZATION OF THIS CHAPTER 641
16.2 HOW TO SOLVE AN ACTUAL SOURCE SEPARATION PROBLEM 642 16.2.1 BLIND
OR SEMI-BLIND 642 16.2.2 ICAFORBSS 643 16.2.3 PRACTICAL USE OF BSS AND
ASSOCIATED ISSUES 644 16.3 OVERFITTING AND ROBUSTNESS 645 16.3.1
OVERFITTING 645 16.3.2 ROBUSTNESS 646 16.4 ILLUSTRATION WITH
ELECTROMAGNETIC TRANSMISSION SYSTEMS 648 16.4.1 A VARIETY OF SOURCE
NATURES AND MIXTURE CONFIGURATIONS 648 16.4.2 A CASE STUDY ON
RADIO-FREQUENCY IDENTIFICATION (RFID) 649 16.4.3 A SYSTEM WITH
MULTI-PATH IONOSPHERIC PROPAGATION 655 16.4.4 USING OTHER SIGNAL
PROPERTIES 658 16.5 EXAMPLE: ANALYSIS OF MARS HYPERSPECTRAL IMAGES 658
16.5.1 PHYSICAL MODEL OF HYPERSPECTRAL IMAGES 658 16.5.2 DECOMPOSITION
MODELS BASED ON ICA 660 16.5.3 REFERENCE DATA AND CLASSIFICATION 661
16.5.4 ICA RESULTS ON HYPERSPECTRAL IMAGES 662 16.5.5 DISCUSSION 665
16.5.6 BEYOND ICA: SEMI-BLIND SOURCE SEPARATION 665 16.5.7 CONCLUSION
666 1 6.6 MONO- VS MULTI-DIMENSIONAL SOURCES AND MIXTURES 668 16.6.1
TIME, SPACE AND WAVELENGTH COORDINATES 668 16.6.2 ANALYZING VIDEO FRAMES
FROM CORTICAL TISSUES 670 16.6.3 EXTRACTING COMPONENTS FROM A TIME
SERIES OF ASTROPHYSICAL LUMINANCE IMAGES 671 16.7 USING PHYSICAL MIXTURE
MODELS OR NOT 672 16.7.1 MOTHER VS FETUS HEARTBEAT SEPARATION FROM
MULTI-CHANNEL ECG RECORDINGS 672 16.7.2 ANALYSIS OF HEART CONTROL FROM
SINGLE-CHANNEL ECG 674 16.7.3 ADDITIONAL COMMENTS ABOUT PERFORMANCE
EVALUATION 676 16.8 SOME CONCLUSIONS AND AVAILABLE TOOLS 676 REFERENCES
677 CONTENTS XVII CHAPTER 17 APPLICATION TO TELECOMMUNICATIONS 683 17.1
INTRODUCTION 683 17.2 DATA MODEL, STATISTICS AND PROBLEM FORMULATION 687
17.2.1 OBSERVATION MODEL 687 17.2.2 DATA STATISTICS 690 17.2.3
FORMULATION OF THE PROBLEM 696 17.3 POSSIBLE METHODS 696 17.3.1 TREATING
THE MIXTURE AS A CONVOLUTIVE ONE 696 17.3.2 TREATING THE MIXTURE AS AN
INSTANTANEOUS ONE 709 17.4 ULTIMATE SEPARATORS OF INSTANTANEOUS MIXTURES
712 17.4.1 SOURCE SEPARATOR PERFORMANCE 712 17.4.2 ULTIMATE SEPARATOR
713 17.4.3 ULTIMATE PERFORMANCE 714 17.5 BLIND SEPARATORS OF
INSTANTANEOUS MIXTURES 716 17.5.1 JADE FOR STATIONARY UNCORRELATED PATHS
716 17.5.2 JADE FOR CYCLOSTATIONARY UNCORRELATED PATHS 718 17.5.3 JADE
FOR CYCLOSTATIONARY CORRELATED PATHS 723 17.5.4 PERFORMANCE ILLUSTRATION
724 17.6 INSTANTANEOUS APPROACH VERSUS CONVOLUTIVE APPROACH: SIMULATION
RESULTS 726 17.6.1 BSS ALGORITHMS AND MEASURES OF PERFORMANCE 726 17.6.2
PERFORMANCES 727 1 7.7 CONCLUSION 729 REFERENCES 730 CHAPTER 18
BIOMEDICAL APPLICATIONS 737 18.1 INTRODUCTION 737 18.2 ONE DECADE OF
ICA-BASED BIOMEDICAL DATA PROCESSING 739 18.2.1 ELECTROMAGNETIC
RECORDINGS FOR FUNCTIONAL BRAIN IMAGING. 739 18.2.2 ELECTROCARDIOGRAM
SIGNAL ANALYSIS 746 18.2.3 OTHER APPLICATION FIELDS 757 18.3 NUMERICAL
COMPLEXITY OF ICA ALGORITHMS 758 18.3.1 GENERAL TOOLS 759 18.3.2
COMPLEXITY OF SEVERAL ICA ALGORITHMS 760 18.4 PERFORMANCE ANALYSIS FOR
BIOMEDICAL SIGNALS 763 18.4.1 COMPARATIVE PERFORMANCE ANALYSIS FOR
SYNTHETIC SIGNALS 764 18.4.2 ICA OF REAL DATA 771 18.5 CONCLUSION 772
REFERENCES 772 XVIII CONTENTS CHAPTER 19 AUDIO APPLICATIONS 779 19.1
AUDIO MIXTURES AND SEPARATION OBJECTIVES 779 19.1.1 RECORDED MIXTURES
780 19.1.2 SYNTHESIZED MIXTURES 782 19.1.3 SEPARATION OBJECTIVES AND
PERFORMANCE EVALUATION 783 19.2 USABLE PROPERTIES OF AUDIO SOURCES 787
19.2.1 INDEPENDENCE 787 19.2.2 SPARSITY 788 19.3 AUDIO APPLICATIONS OF
CONVOLUTIVE ICA 790 19.3.1 MULTICHANNEL FILTERING 790 19.3.2 TIME-DOMAIN
CONVOLUTIVE ICA 794 19.3.3 FREQUENCY-DOMAIN CONVOLUTIVE ICA 799 19.4
AUDIO APPLICATIONS OF SCA 806 19.4.1 TIME-FREQUENCY MASKING 807 19.4.2
INSTANTANEOUS SCA 808 19.4.3 CONVOLUTIVE SCA 810 19.5 CONCLUSION 814
REFERENCES 815 GLOSSARY 821 INDEX 823 ETG-FACHBERICHT ISOLIERSYSTEME BEI
REPETIERENDEN IMPULSFOERMIGEN SPANNUNGSBEANSPRUCHUNGEN VORTRAEGE DES
ETG-WORKSHOPS AM 8. JULI 2009 IN HANNOVER VERANSTALTER:
ENERGIETECHNISCHE GESELLSCHAFT IM VDE (ETG) FACHBEREICH *Q2 WERKSTOFFE,
ISOLIERSYSTEME UND DIAGNOSTIK" IN ZUSAMMENARBEIT MIT * IEEE GERMAN
CHAPTER DER POWER & ENERGY SOCIETY * DEUTSCHES KOMITEE DER CIGRE
WISSENSCHAFTLICHER TAGUNGSLEITER: PROF. DR.-ING. ERNST GOCKENBACH,
GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET HANNOVER MIT CD-ROM VDE VERLAG
GMBH * BERLIN * OFFENBACH ISOLIERSYSTEME, 08.07.2009 IN HANNOVER * VDE
VERLAG GMBH * BERLIN * OFFENBACH INHALT 1 FAST REPETITIVE VOLTAGES IN
THE POWER SYSTEM 7 T. BENGTSSON, F. DIJKHUIZEN, L. MING, F. SAHLEN, L.
LILJESTRAND, D. BORMANN, R. PAPAZYAN, M. DAHLGREN, ABB CORPORATE
RESEARCH, VAESTERAES, SWEDEN 2 CIGRE- UND IEC-AKTIVITAETEN 17 PROF.
DR.-ING. ERNST GOCKENBACH, GOTTFRIED WILHELM LEIBNIZ UNIVERSITAET
HANNOVER 3 KRIECHSTROMFESTIGKEIT VON POLYMEREN ISOLIERSTOFFEN BEI
ERHOEHTEN FREQUENZEN 21 M. HAIM, K. STIMPER, UNIVERSITAET DER BUNDESWEHR
MUENCHEN, NEUBIBERG; R. BARSCH, HOCHSCHULE ZITTAU/GOERLITZ 4 BEANSPRUCHUNG
VON ISOLATOREN BEI SENDEFREQUENZEN 29 J. SEIFERT, LAPP INSULATOR GMBH &
CO. KG, WUNSIEDEL 5 DIELEKTRISCHE PROBLEME BEI LEISTUNGSELEKTRONIK 41 F.
JENAU, TH. MERTENS, S. AZER, TECHNISCHE UNIVERSITAET DORTMUND 6
BEANSPRUCHUNG VON ELEMENTEN IN DER NIEDERSPANNUNGSTECHNIK 49 R. PLATH,
H. EMANUEL, OMICRON ELECTRONICS GMBH, BERLIN 7 WIRKUNGSWEISE UND
ALTERUNGSVERHALTEN VON METALLOXIDVARISTOREN UM UEBERSPANNUNGSSCHUTZ IN
UMRICHTERGESPEISTEN ANTRIEBSSYSTEMEN 57 A. ROCKS, V. HINRICHSEN,
TECHNISCHE UNIVERSITAET DARMSTADT 8 WICKLUNGSISOLIERUNG
UMRICHTERGESPEISTER ROTIERENDER MASCHINEN - BELASTUNG, FESTIGKEIT,
QUALIFIZIERUNG 65 M. KAUFHOLD, SIEMENS AG, NUERNBERG 9 BREAKDOWN STRENGTH
OF OIL/PAPER INSULATION SYSTEMS AT SINUSOIDAL VOLTAGE STRESSES AT HIGHER
FREQUENCIES 81 M. NAGEL, *. LEIBFRIED, TECHNISCHE UNIVERSITAET KARLSRUHE |
| any_adam_object | 1 |
| author | Comon, Pierre |
| author2 | Comon, Pierre Jutten, Christian 1954- |
| author2_role | edt edt |
| author2_variant | p c pc c j cj |
| author_GND | (DE-588)123677339X |
| author_facet | Comon, Pierre Comon, Pierre Jutten, Christian 1954- |
| author_role | aut |
| author_sort | Comon, Pierre |
| author_variant | p c pc |
| building | Verbundindex |
| bvnumber | BV036092844 |
| callnumber-first | T - Technology |
| callnumber-label | TK5102 |
| callnumber-raw | TK5102.9 |
| callnumber-search | TK5102.9 |
| callnumber-sort | TK 45102.9 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | SK 990 |
| ctrlnum | (OCoLC)430842957 (DE-599)BVBBV036092844 |
| dewey-full | 621.38223 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.38223 |
| dewey-search | 621.38223 |
| dewey-sort | 3621.38223 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik |
| edition | First edition |
| format | Book |
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| id | DE-604.BV036092844 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-20T05:14:54Z |
| institution | BVB |
| isbn | 9780123747266 0123747260 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018983418 |
| oclc_num | 430842957 |
| open_access_boolean | |
| owner | DE-706 DE-355 DE-BY-UBR |
| owner_facet | DE-706 DE-355 DE-BY-UBR |
| physical | XXIV, 831 Seiten 25 cm |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Elsevier |
| record_format | marc |
| spelling | Handbook of blind source separation independent component analysis and applications edited by P. Comon and C. Jutten First edition Amsterdam ; Boston ; Heidelberg ; London ; New York ; Oxford ; Paris ; San Diego ; San Francisco ; Singapore ; Sydney ; Tokyo Elsevier 2010 XXIV, 831 Seiten 25 cm txt rdacontent n rdamedia nc rdacarrier Blind source separation Numerische Mathematik (DE-588)4042805-9 gnd rswk-swf Signaltrennung (DE-588)4181278-5 gnd rswk-swf Unabhängige Komponentenanalyse (DE-588)4812492-8 gnd rswk-swf Signalanalyse (DE-588)4181260-8 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Unabhängige Komponentenanalyse (DE-588)4812492-8 s Signaltrennung (DE-588)4181278-5 s Signalanalyse (DE-588)4181260-8 s Numerische Mathematik (DE-588)4042805-9 s Algorithmus (DE-588)4001183-5 s DE-604 Comon, Pierre edt Jutten, Christian 1954- (DE-588)123677339X edt GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018983418&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Comon, Pierre Handbook of blind source separation independent component analysis and applications Blind source separation Numerische Mathematik (DE-588)4042805-9 gnd Signaltrennung (DE-588)4181278-5 gnd Unabhängige Komponentenanalyse (DE-588)4812492-8 gnd Signalanalyse (DE-588)4181260-8 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4042805-9 (DE-588)4181278-5 (DE-588)4812492-8 (DE-588)4181260-8 (DE-588)4001183-5 |
| title | Handbook of blind source separation independent component analysis and applications |
| title_auth | Handbook of blind source separation independent component analysis and applications |
| title_exact_search | Handbook of blind source separation independent component analysis and applications |
| title_full | Handbook of blind source separation independent component analysis and applications edited by P. Comon and C. Jutten |
| title_fullStr | Handbook of blind source separation independent component analysis and applications edited by P. Comon and C. Jutten |
| title_full_unstemmed | Handbook of blind source separation independent component analysis and applications edited by P. Comon and C. Jutten |
| title_short | Handbook of blind source separation |
| title_sort | handbook of blind source separation independent component analysis and applications |
| title_sub | independent component analysis and applications |
| topic | Blind source separation Numerische Mathematik (DE-588)4042805-9 gnd Signaltrennung (DE-588)4181278-5 gnd Unabhängige Komponentenanalyse (DE-588)4812492-8 gnd Signalanalyse (DE-588)4181260-8 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Blind source separation Numerische Mathematik Signaltrennung Unabhängige Komponentenanalyse Signalanalyse Algorithmus |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018983418&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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