Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley
2009
|
| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXI, 477 S. Ill., graf. Darst. |
| ISBN: | 9780470746660 |
Internformat
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| 245 | 1 | 0 | |a Nonnegative matrix and tensor factorizations |b applications to exploratory multi-way data analysis and blind source separation |c Andrzej Cichocki ... [et al.] |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2009 | |
| 300 | |a XXI, 477 S. |b Ill., graf. Darst. | ||
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| 500 | |a Includes bibliographical references and index | ||
| 650 | 0 | |a Computer algorithms | |
| 650 | 0 | |a Data mining | |
| 650 | 0 | |a Machine learning | |
| 650 | 0 | |a Data structures (Computer science) | |
| 650 | 7 | |a Algorithmes |2 ram | |
| 650 | 7 | |a Exploration de données |2 ram | |
| 650 | 7 | |a Structures de données (informatique) |2 ram | |
| 650 | 4 | |a Computer algorithms | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Data structures (Computer science) | |
| 650 | 4 | |a Machine learning | |
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Datensatz im Suchindex
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| adam_text | NONNEGATIVE MATRIX AND TENSOR FACTORIZATIONS APPLICATIONS TO EXPLORATORY
MULTI-WAY DATA ANALYSIS AND BLIND SOURCE SEPARATION ANDRZEJ CICHOCKI
LABORATORY FOR ADVANCED BRAIN SIGNAL PROCESSING, RIKEN BRAIN SCIENCE
INSTITUTE, JAPAN; AND WARSAW UNIVERSITY OF TECHNOLOGY AND SYSTEMS
RESEARCH INSTITUTE, PAN, POLAND RAFAL ZDUNEK INSTITUTE OF
TELECOMMUNICATIONS, TELEINFORMATICS AND ACOUSTICS, WROCLAW UNIVERSITY OF
TECHNOLOGY, POLAND; AND RIKEN BRAIN SCIENCE INSTITUTE, JAPAN ANH HUY
PHAN LABORATORY FOR ADVANCED BRAIN SIGNAL PROCESSING, RIKEN BRAIN
SCIENCE INSTITUTE, JAPAN SHUN-ICHI AMARI RESEARCH UNIT FOR MATHEMATICAL
NEUROSCIENCE, RIKEN BRAIN SCIENCE INSTITUTE, JAPAN WILEY A JOHN WILEY
AND SONS, LTD, PUBLICATION CONTENTS PREFACE XI ACKNOWLEDGMENTS XV
GLOSSARY OF SYMBOLS AND ABBREVIATIONS XVII 1 INTRODUCTION - PROBLEM
STATEMENTS AND MODELS 1 1. 1 BLIND SOURCE SEPARATION AND LINEAR
GENERALIZED COMPONENT ANALYSIS 2 1.2 MATRIX FACTORIZATION MODELS WITH
NONNEGATIVITY AND SPARSITY CONSTRAINTS 7 1.2.1 WHY NONNEGATIVITY AND
SPARSITY CONSTRAINTS? 7 1.2.2 BASIC NMF MODEL 8 1.2.3 SYMMETRIC NMF 9
1.2.4 SEMI-ORTHOGONAL NMF 10 1.2.5 SEMI-NMF AND NONNEGATIVE
FACTORIZATION OF ARBITRARY MATRIX 10 1.2.6 THREE-FACTOR NMF 10 1.2.7 NMF
WITH OFFSET (AFFINE NMF) 13 1.2.8 MULTI-LAYER NMF 14 1.2.9 SIMULTANEOUS
NMF 14 1.2.10 PROJECTIVE AND CONVEX NMF 15 1.2.11 KERNEL NMF 16 1.2.12
CONVOLUTIVE NMF 16 1.2.13 OVERLAPPING NMF 17 1.3 BASIC APPROACHES TO
ESTIMATE PARAMETERS OF STANDARD NMF 18 1.3.1 LARGE-SCALE NMF 21 1.3.2
NON-UNIQUENESS OF NMF AND TECHNIQUES TO ALLEVIATE THE AMBIGUITY PROBLEM
22 1.3.3 INITIALIZATION OF NMF 24 1.3.4 STOPPING CRITERIA 25 1.4 TENSOR
PROPERTIES AND BASIS OF TENSOR ALGEBRA 26 1.4.1 TENSORS (MULTI-WAY
ARRAYS) - PRELIMINARIES 26 1.4.2 SUBARRAYS, TUBES AND SLICES 27 1.4.3
UNFOLDING - MATRICIZATION 28 1.4.4 VECTORIZATION 31 1.4.5 OUTER,
KRONECKER, KHATRI-RAO AND HADAMARD PRODUCTS 32 1.4.6 MODE-N
MULTIPLICATION OF TENSOR BY MATRIX AND TENSOR BY VECTOR, CONTRACTED
TENSOR PRODUCT 34 1.4.7 SPECIAL FORMS OF TENSORS 38 1.5 TENSOR
DECOMPOSITIONS AND FACTORIZATIONS 39 1.5.1 WHY MULTI-WAY ARRAY
DECOMPOSITIONS AND FACTORIZATIONS? 40 1.5.2 PARAFAC AND NONNEGATIVE
TENSOR FACTORIZATION 42 1.5.3 NTF1 MODEL 47 VI CONTENTS 1.5.5 1.5.6
1.5.7 1.5.8 1.5.9 1.5.10 1.5.11 NTF2 MODEL 49 INDIVIDUAL DIFFERENCES IN
SCALING (INDSCAL) AND IMPLICIT SLICE CANONICAL DECOMPOSITION MODEL
(IMCAND) 52 SHIFTED PARAFAC AND CONVOLUTIVE NTF 53 NONNEGATIVE TUCKER
DECOMPOSITIONS 55 BLOCK COMPONENT DECOMPOSITIONS 59 BLOCK-ORIENTED
DECOMPOSITIONS 62 PARATUCK2 AND DEDICOM MODELS 63 HIERARCHICAL TENSOR
DECOMPOSITION 65 1.6 DISCUSSION AND CONCLUSIONS 66 APPENDIX L.A:
UNIQUENESS CONDITIONS FOR THREE-WAY TENSOR FACTORIZATIONS 66 APPENDIX
LB: SINGULAR VALUE DECOMPOSITION (SVD) AND PRINCIPAL COMPONENT ANALYSIS
(PCA) WITH SPARSITY AND/OR NONNEGATIVITY CONSTRAINTS 67 L.B.L STANDARD
SVD AND PCA 68 1.B.2 SPARSE PCA 70 1.B.3 NONNEGATIVE PCA 71 APPENDIX 1
.C: DETERMINING A TRUE NUMBER OF COMPONENTS 71 APPENDIX L.D: NONNEGATIVE
RANK FACTORIZATION USING WEDDERBORN THEOREM - ESTIMATION OF THE NUMBER
OF COMPONENTS 74 REFERENCES 75 2 SIMILARITY MEASURES AND GENERALIZED
DIVERGENCES 81 2. 1 ERROR-INDUCED DISTANCE AND ROBUST REGRESSION
TECHNIQUES 82 2.2 ROBUST ESTIMATION 84 2.3 CSISZAER DIVERGENCES 90 2.4
BREGMAN DIVERGENCE 96 2.4.1 BREGMAN MATRIX DIVERGENCES 103 2.5
ALPHA-DIVERGENCES 104 2.5.1 ASYMMETRIC ALPHA-DIVERGENCES 104 2.5.2
SYMMETRIC ALPHA-DIVERGENCES 110 2.6 BETA-DIVERGENCES 1 12 2.7
GAMMA-DIVERGENCES 116 2.8 DIVERGENCES DERIVED FROM TSALLIS AND RENYI
ENTROPY 118 2.8.1 CONCLUDING REMARKS 119 APPENDIX 2.A: INFORMATION
GEOMETRY, CANONICAL DIVERGENCE, AND PROJECTION 120 2. A. 1 SPACE OF
PROBABILITY DISTRIBUTIONS 120 2.A.2 GEOMETRY OF SPACE OF POSITIVE
MEASURES 123 APPENDIX 2.B: PROBABILITY DENSITY FUNCTIONS FOR VARIOUS
DISTRIBUTIONS 125 REFERENCES 127 3 MULTIPLICATIVE ITERATIVE ALGORITHMS
FOR NMF WITH SPARSITY CONSTRAINTS 131 3.1 EXTENDED ISRA AND EMML
ALGORITHMS: REGULARIZATION AND SPARSITY 132 3.1.1 MULTIPLICATIVE NMF
ALGORITHMS BASED ON THE SQUARED EUCLIDEAN DISTANCE 132 3.1.2
MULTIPLICATIVE NMF ALGORITHMS BASED ON KULLBACK-LEIBLER I-DIVERGENCE 139
3.2 MULTIPLICATIVE ALGORITHMS BASED ON ALPHA-DIVERGENCE 143 3.2.1
MULTIPLICATIVE ALPHA NMF ALGORITHM 143 3.2.2 GENERALIZED MULTIPLICATIVE
ALPHA NMF ALGORITHMS 147 3.3 ALTERNATING SMART: SIMULTANEOUS
MULTIPLICATIVE ALGEBRAIC RECONSTRUCTION TECHNIQUE 148 3.3.1 ALPHA SMART
ALGORITHM 148 3.3.2 GENERALIZED SMART ALGORITHMS 150 CONTENTS 3.4
MULTIPLICATIVE NMF ALGORITHMS BASED ON BETA-DIVERGENCE 151 3.4.1
MULTIPLICATIVE BETA NMF ALGORITHM 151 3.4.2 MULTIPLICATIVE ALGORITHM
BASED ON THE ITAKURA-SAITO DISTANCE 156 3.4.3 GENERALIZED MULTIPLICATIVE
BETA ALGORITHM FOR NMF 156 3.5 ALGORITHMS FOR SEMI-ORTHOGONAL NMF AND
ORTHOGONAL THREE-FACTOR NMF 157 3.6 MULTIPLICATIVE ALGORITHMS FOR AFFINE
NMF 159 3.7 MULTIPLICATIVE ALGORITHMS FOR CONVOLUTIVE NMF 160 3.7.1
MULTIPLICATIVE ALGORITHM FOR CONVOLUTIVE NMF BASED ON ALPHA-DIVERGENCE
162 3.7.2 MULTIPLICATIVE ALGORITHM FOR CONVOLUTIVE NMF BASED ON
BETA-DIVERGENCE 162 3.7.3 EFFICIENT IMPLEMENTATION OF CNMF ALGORITHM 165
3.8 SIMULATION EXAMPLES FOR STANDARD NMF 166 3.9 EXAMPLES FOR AFFINE NMF
170 3.10 MUSIC ANALYSIS AND DECOMPOSITION USING CONVOLUTIVE NMF 176 3.11
DISCUSSION AND CONCLUSIONS 184 APPENDIX 3.A: FAST ALGORITHMS FOR
LARGE-SCALE DATA 187 3.A. 1 RANDOM BLOCK-WISE PROCESSING APPROACH -
LARGE-SCALE NMF 187 3.A.2 MULTI-LAYER PROCEDURE 188 3.A.3 PARALLEL
PROCESSING 188 APPENDIX 3.B: PERFORMANCE EVALUATION 188 3.B.1
SIGNAL-TO-INTERFERENCE-RATIO - SIR 188 3.B.2 PEAK SIGNAL-TO-NOISE-RATIO
(PSNR) 190 APPENDIX 3.C: CONVERGENCE ANALYSIS OF THE MULTIPLICATIVE
ALPHA NMF ALGORITHM 191 APPENDIX 3.D: MATLAB IMPLEMENTATION OF THE
MULTIPLICATIVE NMF ALGORITHMS 193 3.D.1 ALPHA ALGORITHM 193 3.D.2 SMART
ALGORITHM 195 3.D.3 ISRA ALGORITHM FOR NMF 197 APPENDIX 3.E: ADDITIONAL
MATLAB FUNCTIONS 198 3.E.1 MULTI-LAYER NMF 198 3.E.2 MC ANALYSIS WITH
DISTRIBUTED COMPUTING TOOL 199 REFERENCES 199 4 ALTERNATING LEAST
SQUARES AND RELATED ALGORITHMS FOR NMF AND SCA PROBLEMS 203 4.1 STANDARD
ALS ALGORITHM 203 4.1.1 MULTIPLE LINEAR REGRESSION - VECTORIZED VERSION
OF ALS UPDATE FORMULAS 206 4.1.2 WEIGHTED ALS 206 4.2 METHODS FOR
IMPROVING PERFORMANCE AND CONVERGENCE SPEED OF ALS ALGORITHMS 207 4.2.1
ALS ALGORITHM FOR VERY LARGE-SCALE NMF 207 4.2.2 ALS ALGORITHM WITH
LINE-SEARCH 208 4.2.3 ACCELERATION OF ALS ALGORITHM VIA SIMPLE
REGULARIZATION 208 4.3 ALS ALGORITHM WITH FLEXIBLE AND GENERALIZED
REGULARIZATION TERMS 209 4.3.1 ALS WITH TIKHONOV TYPE REGULARIZATION
TERMS 210 4.3.2 ALS ALGORITHMS WITH SPARSITY CONTROL AND DECORRELATION
211 4.4 COMBINED GENERALIZED REGULARIZED ALS ALGORITHMS 212 4.5
WANG-HANCEWICZ MODIFIED ALS ALGORITHM 213 4.6 IMPLEMENTATION OF
REGULARIZED ALS ALGORITHMS FOR NMF 213 4.7 HALS ALGORITHM AND ITS
EXTENSIONS 214 4.7.1 PROJECTED GRADIENT LOCAL HIERARCHICAL ALTERNATING
LEAST SQUARES (HALS) ALGORITHM 214 4.7.2 EXTENSIONS AND IMPLEMENTATIONS
OF THE HALS ALGORITHM 216 4.7.3 FAST HALS NMF ALGORITHM FOR LARGE-SCALE
PROBLEMS 217 VLLL CONTENTS 4.7.4 HALS NMF ALGORITHM WITH SPARSITY,
SMOOTHNESS AND UNCORRELATEDNESS CONSTRAINTS 220 4.7.5 HALS ALGORITHM FOR
SPARSE COMPONENT ANALYSIS AND FLEXIBLE COMPONENT ANALYSIS 222 4.7.6
SIMPLIFIED HALS ALGORITHM FOR DISTRIBUTED AND MULTI-TASK COMPRESSED
SENSING 227 4.7.7 GENERALIZED HALS-CS ALGORITHM 231 4.7.8 GENERALIZED
HALS ALGORITHMS USING ALPHA-DIVERGENCE 233 4.7.9 GENERALIZED HALS
ALGORITHMS USING BETA-DIVERGENCE 234 4.8 SIMULATION RESULTS 236 4.8.1
UNDERDETERMINED BLIND SOURCE SEPARATION EXAMPLES 236 4.8.2 NMF WITH
SPARSENESS, ORTHOGONALITY AND SMOOTHNESS CONSTRAINTS 237 4.8.3
SIMULATIONS FOR LARGE-SCALE NMF 239 4.8.4 ILLUSTRATIVE EXAMPLES FOR
COMPRESSED SENSING 241 4.9 DISCUSSION AND CONCLUSIONS 249 APPENDIX 4.A:
MATLAB SOURCE CODE FOR ALS ALGORITHM 252 APPENDIX 4.B: MATLAB SOURCE
CODE FOR REGULARIZED ALS ALGORITHMS 253 APPENDIX 4.C: MATLAB SOURCE CODE
FOR MIXED ALS-HALS ALGORITHMS 256 APPENDIX 4.D: MATLAB SOURCE CODE FOR
HALS CS ALGORITHM 259 APPENDIX 4.E: ADDITIONAL MATLAB FUNCTIONS 261
REFERENCES 264 5 PROJECTED GRADIENT ALGORITHMS 267 5.1 OBLIQUE PROJECTED
LANDWEBER (OPL) METHOD 268 5.2 LIN S PROJECTED GRADIENT (LPG) ALGORITHM
WITH ARMIJO RULE 270 5.3 BARZILAI-BORWEIN GRADIENT PROJECTION FOR SPARSE
RECONSTRUCTION (GPSR-BB) 271 5.4 PROJECTED SEQUENTIAL SUBSPACE
OPTIMIZATION (PSESOP) 273 5.5 INTERIOR POINT GRADIENT (IPG) ALGORITHM
275 5.6 INTERIOR POINT NEWTON (IPN) ALGORITHM 276 5.7 REGULARIZED
MINIMAL RESIDUAL NORM STEEPEST DESCENT ALGORITHM (RMRNSD) 279 5.8
SEQUENTIAL COORDINATE-WISE ALGORITHM (SCWA) 281 5.9 SIMULATIONS 283 5.10
DISCUSSIONS 289 APPENDIX 5.A: STOPPING CRITERIA 290 APPENDIX 5.B: MATLAB
SOURCE CODE FOR LIN S PG ALGORITHM 292 REFERENCES 293 6 QUASI-NEWTON
ALGORITHMS FOR NONNEGATIVE MATRIX FACTORIZATION 295 6.1 PROJECTED
QUASI-NEWTON OPTIMIZATION 296 6.1.1 PROJECTED QUASI-NEWTON FOR FROBENIUS
NORM 296 6.1.2 PROJECTED QUASI-NEWTON FOR ALPHA-DIVERGENCE 298 6.1.3
PROJECTED QUASI-NEWTON FOR BETA-DIVERGENCE 303 6.1.4 PRACTICAL
IMPLEMENTATION 305 6.2 GRADIENT PROJECTION CONJUGATE GRADIENT 305 6.3
FNMA ALGORITHM 308 6.4 NMF WITH QUADRATIC PROGRAMMING 310 6.4.1
NONLINEAR PROGRAMMING 311 6.4.2 QUADRATIC PROGRAMMING 312 6.4.3
TRUST-REGION SUBPROBLEM 314 6.4.4 UPDATES FOR A 316 6.5 HYBRID UPDATES
318 CONTENTS IX 6.6 NUMERICAL RESULTS 319 6.7 DISCUSSIONS 323 APPENDIX
6. A: GRADIENT AND HESSIAN OF COST FUNCTIONS 324 APPENDIX 6.B: MATLAB
SOURCE CODES 325 REFERENCES 333 7 MULTI-WAY ARRAY (TENSOR)
FACTORIZATIONS AND DECOMPOSITIONS 337 7.1 LEARNING RULES FOR THE
EXTENDED THREE-WAY NTF1 PROBLEM 337 7.1.1 BASIC APPROACHES FOR THE
EXTENDED NTF1 MODEL 338 7.1.2 ALS ALGORITHMS FOR NTF1 340 7.1.3
MULTIPLICATIVE ALPHA AND BETA ALGORITHMS FOR THE NTF1 MODEL 341 7.1.4
MULTI-LAYER NTF1 STRATEGY 343 7.2 ALGORITHMS FOR THREE-WAY STANDARD AND
SUPER SYMMETRIC NONNEGATIVE TENSOR FACTORIZATION 344 7.2.1
MULTIPLICATIVE NTF ALGORITHMS BASED ON ALPHA- AND BETA-DIVERGENCES 345
7.2.2 SIMPLE ALTERNATIVE APPROACHES FOR NTF AND SSNTF 350 7.3
NONNEGATIVE TENSOR FACTORIZATIONS FOR HIGHER-ORDER ARRAYS 351 7.3.1
ALPHA NTF ALGORITHM 353 7.3.2 BETA NTF ALGORITHM 355 7.3.3 FAST HALS NTF
ALGORITHM USING SQUARED EUCLIDEAN DISTANCE 355 7.3.4 GENERALIZED HALS
NTF ALGORITHMS USING ALPHA- AND BETA-DIVERGENCES 358 7.3.5 TENSOR
FACTORIZATION WITH ADDITIONAL CONSTRAINTS 360 7.4 ALGORITHMS FOR
NONNEGATIVE AND SEMI-NONNEGATIVE TUCKER DECOMPOSITIONS 361 7.4.1 HIGHER
ORDER SVD (HOSVD) AND HIGHER ORDER ORTHOGONAL ITERATION (HOOI)
ALGORITHMS 362 7.4.2 ALS ALGORITHM FOR NONNEGATIVE TUCKER DECOMPOSITION
365 7.4.3 HOSVD, HOOI AND ALS ALGORITHMS AS INITIALIZATION TOOLS FOR
NONNEGATIVE TENSOR DECOMPOSITION 366 7.4.4 MULTIPLICATIVE ALPHA
ALGORITHMS FOR NONNEGATIVE TUCKER DECOMPOSITION 366 7.4.5 BETA NTD
ALGORITHM 370 7.4.6 LOCAL ALS ALGORITHMS FOR NONNEGATIVE TUCKER
DECOMPOSITIONS 370 7.4.7 SEMI-NONNEGATIVE TUCKER DECOMPOSITION 374 7.5
NONNEGATIVE BLOCK-ORIENTED DECOMPOSITION 375 7.5.1 MULTIPLICATIVE
ALGORITHMS FOR NBOD 376 7.6 MULTI-LEVEL NONNEGATIVE TENSOR DECOMPOSITION
- HIGH ACCURACY COMPRESSION AND APPROXIMATION 377 7.7 SIMULATIONS AND
ILLUSTRATIVE EXAMPLES 378 7.7.1 EXPERIMENTS FOR NONNEGATIVE TENSOR
FACTORIZATIONS 378 7.7.2 EXPERIMENTS FOR NONNEGATIVE TUCKER
DECOMPOSITION 384 7.7.3 EXPERIMENTS FOR NONNEGATIVE BLOCK-ORIENTED
DECOMPOSITION 392 7.7.4 MULTI-WAY ANALYSIS OF HIGH DENSITY ARRAY EEG -
CLASSIFICATION OF EVENT RELATED POTENTIALS 395 7.7.5 APPLICATION OF
TENSOR DECOMPOSITIONS IN BRAIN COMPUTER INTERFACE - CLASSIFICATION OF
MOTOR IMAGERY TASKS 404 7.7.6 IMAGE AND VIDEO APPLICATIONS 409 7.8
DISCUSSION AND CONCLUSIONS 412 APPENDIX 7.A: EVALUATION OF INTERACTIONS
AND RELATIONSHIPS AMONG HIDDEN COMPONENTS FOR NTD MODEL 415 APPENDIX
7.B: COMPUTATION OF A REFERENCE TENSOR 416 APPENDIX 7.C: TRILINEAR AND
DIRECT TRILINEAR DECOMPOSITIONS FOR EFFICIENT INITIALIZATION 418
CONTENTS APPENDIX 7.D: MATLAB SOURCE CODE FOR ALPHA NTD ALGORITHM 420
APPENDIX 7.E: MATLAB SOURCE CODE FOR BETA NTD ALGORITHM 421 APPENDIX
7.F: MATLAB SOURCE CODE FOR HALS NTD ALGORITHM 423 APPENDIX 7.G: MATLAB
SOURCE CODE FOR ALS NTF1 ALGORITHM 425 APPENDIX 7.H: MATLAB SOURCE CODE
FOR ISRA BOD ALGORITHM 426 APPENDIX 7.1: ADDITIONAL MATLAB FUNCTIONS 427
REFERENCES 429 8 SELECTED APPLICATIONS 433 8.1 CLUSTERING 433 8.1.1
SEMI-BINARY NMF 434 8.1.2 NMF VS. SPECTRAL CLUSTERING 435 8.1.3
CLUSTERING WITH CONVEX NMF 436 8.1.4 APPLICATION OF NMF TO TEXT MINING
438 8.1.5 EMAIL SURVEILLANCE 440 8.2 CLASSIFICATION 442 8.2.1 MUSICAL
INSTRUMENT CLASSIFICATION 442 8.2.2 IMAGE CLASSIFICATION 443 8.3
SPECTROSCOPY 447 8.3.1 RAMAN SPECTROSCOPY 447 8.3.2 FLUORESCENCE
SPECTROSCOPY 449 8.3.3 HYPERSPECTRAL IMAGING 450 8.3.4 CHEMICAL SHIFT
IMAGING 452 8.4 APPLICATION OF NMF FOR ANALYZING MICROARRAY DATA 455
8.4.1 GENE EXPRESSION CLASSIFICATION 455 8.4.2 ANALYSIS OF TIME COURSE
MICROARRAY DATA 459 REFERENCES 467 INDEX 473
|
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| id | DE-604.BV035679913 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:43:15Z |
| institution | BVB |
| isbn | 9780470746660 |
| language | English |
| lccn | 2009016049 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017734193 |
| oclc_num | 320432452 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-706 DE-83 DE-29T |
| owner_facet | DE-473 DE-BY-UBG DE-355 DE-BY-UBR DE-706 DE-83 DE-29T |
| physical | XXI, 477 S. Ill., graf. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | Wiley |
| record_format | marc |
| spelling | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation Andrzej Cichocki ... [et al.] 1. ed. Chichester Wiley 2009 XXI, 477 S. Ill., graf. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Computer algorithms Data mining Machine learning Data structures (Computer science) Algorithmes ram Exploration de données ram Structures de données (informatique) ram Matrizenzerlegung (DE-588)4376303-0 gnd rswk-swf Nichtnegative Matrix (DE-588)4310434-4 gnd rswk-swf Matrizenzerlegung (DE-588)4376303-0 s Nichtnegative Matrix (DE-588)4310434-4 s DE-604 Cichocki, Andrzej Sonstige oth GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017734193&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation Computer algorithms Data mining Machine learning Data structures (Computer science) Algorithmes ram Exploration de données ram Structures de données (informatique) ram Matrizenzerlegung (DE-588)4376303-0 gnd Nichtnegative Matrix (DE-588)4310434-4 gnd |
| subject_GND | (DE-588)4376303-0 (DE-588)4310434-4 |
| title | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation |
| title_auth | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation |
| title_exact_search | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation |
| title_full | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation Andrzej Cichocki ... [et al.] |
| title_fullStr | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation Andrzej Cichocki ... [et al.] |
| title_full_unstemmed | Nonnegative matrix and tensor factorizations applications to exploratory multi-way data analysis and blind source separation Andrzej Cichocki ... [et al.] |
| title_short | Nonnegative matrix and tensor factorizations |
| title_sort | nonnegative matrix and tensor factorizations applications to exploratory multi way data analysis and blind source separation |
| title_sub | applications to exploratory multi-way data analysis and blind source separation |
| topic | Computer algorithms Data mining Machine learning Data structures (Computer science) Algorithmes ram Exploration de données ram Structures de données (informatique) ram Matrizenzerlegung (DE-588)4376303-0 gnd Nichtnegative Matrix (DE-588)4310434-4 gnd |
| topic_facet | Computer algorithms Data mining Machine learning Data structures (Computer science) Algorithmes Exploration de données Structures de données (informatique) Matrizenzerlegung Nichtnegative Matrix |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017734193&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cichockiandrzej nonnegativematrixandtensorfactorizationsapplicationstoexploratorymultiwaydataanalysisandblindsourceseparation |