Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation

501 Pages·2009·14.59 MB·English

by  Andrzej Cichocki

NONNEGATIVE MATRIX
AND TENSOR
FACTORIZATIONS

NONNEGATIVE
MATRIX AND
TENSOR
FACTORIZATIONS
APPLICATIONS TO EXPLORATORY
MULTI-WAY DATA ANALYSIS AND
BLIND SOURCE SEPARATION
AndrzejCichocki
LaboratoryforAdvancedBrainSignalProcessing,RikenBrainScienceInstitute,Japan;andWarsaw
UniversityofTechnologyandSystemsResearchInstitute,PAN,Poland
RafalZdunek
InstituteofTelecommunications,TeleinformaticsandAcoustics,WroclawUniversityofTechnology,
Poland;andRikenBrainScienceInstitute,Japan
AnhHuyPhan
LaboratoryforAdvancedBrainSignalProcessing,RikenBrainScienceInstitute,Japan
Shun-ichiAmari
ResearchUnitforMathematicalNeuroscience,RikenBrainScienceInstitute,Japan
Thiseditionfirstpublished2009
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LibraryofCongressCataloging-in-PublicationData
Nonnegativematrixandtensorfactorizations:applicationstoexploratorymultiwaydataanalysis
andblindsourceseparation/AndrzejCichocki...[etal.].
p.cm.
Includesbibliographicalreferencesandindex.
ISBN978-0-470-74666-0(cloth)
1. Computeralgorithms.2. Datamining.3.Machinelearning4. Datastructures(Computerscience)
I. Cichocki,Andrzej.
QA76.9.A43N652009
005.1–dc22 2009016049
AcataloguerecordforthisbookisavailablefromtheBritishLibrary.
ISBN:978-0-470-74666-0
Setin9/11ptTimesbyThomsonDigital,Noida,India
PrintedinSingaporebyFabulous
Contents
Preface xi
Acknowledgments xv
GlossaryofSymbolsandAbbreviations xvii
1 Introduction–ProblemStatementsandModels 1
1.1 BlindSourceSeparationandLinearGeneralizedComponentAnalysis 2
1.2 MatrixFactorizationModelswithNonnegativityandSparsityConstraints 7
1.2.1 WhyNonnegativityandSparsityConstraints? 7
1.2.2 BasicNMFModel 8
1.2.3 SymmetricNMF 9
1.2.4 Semi-OrthogonalNMF 10
1.2.5 Semi-NMFandNonnegativeFactorizationofArbitraryMatrix 10
1.2.6 Three-factorNMF 10
1.2.7 NMFwithOffset(AffineNMF) 13
1.2.8 Multi-layerNMF 14
1.2.9 SimultaneousNMF 14
1.2.10 ProjectiveandConvexNMF 15
1.2.11 KernelNMF 16
1.2.12 ConvolutiveNMF 16
1.2.13 OverlappingNMF 17
1.3 BasicApproachestoEstimateParametersofStandardNMF 18
1.3.1 Large-scaleNMF 21
1.3.2 Non-uniquenessofNMFandTechniquestoAlleviatetheAmbiguityProblem 22
1.3.3 InitializationofNMF 24
1.3.4 StoppingCriteria 25
1.4 TensorPropertiesandBasisofTensorAlgebra 26
1.4.1 Tensors(Multi-wayArrays)–Preliminaries 26
1.4.2 Subarrays,TubesandSlices 27
1.4.3 Unfolding–Matricization 28
1.4.4 Vectorization 31
1.4.5 Outer,Kronecker,Khatri-RaoandHadamardProducts 32
1.4.6 Mode-nMultiplicationofTensorbyMatrixandTensor
byVector,ContractedTensorProduct 34
1.4.7 SpecialFormsofTensors 38
1.5 TensorDecompositionsandFactorizations 39
1.5.1 WhyMulti-wayArrayDecompositionsandFactorizations? 40
1.5.2 PARAFACandNonnegativeTensorFactorization 42
1.5.3 NTF1Model 47
vi Contents
1.5.4 NTF2Model 49
1.5.5 IndividualDifferencesinScaling(INDSCAL)andImplicitSliceCanonical
DecompositionModel(IMCAND) 52
1.5.6 ShiftedPARAFACandConvolutiveNTF 53
1.5.7 NonnegativeTuckerDecompositions 55
1.5.8 BlockComponentDecompositions 59
1.5.9 Block-OrientedDecompositions 62
1.5.10 PARATUCK2andDEDICOMModels 63
1.5.11 HierarchicalTensorDecomposition 65
1.6 DiscussionandConclusions 66
Appendix1.A:UniquenessConditionsforThree-wayTensorFactorizations 66
Appendix1.B:SingularValueDecomposition(SVD)andPrincipalComponent
Analysis(PCA)withSparsityand/orNonnegativityConstraints 67
1.B.1 StandardSVDandPCA 68
1.B.2 SparsePCA 70
1.B.3 NonnegativePCA 71
Appendix1.C:DeterminingaTrueNumberofComponents 71
Appendix1.D:NonnegativeRankFactorizationUsingWedderbornTheorem–Estimation
oftheNumberofComponents 74
References 75
2 SimilarityMeasuresandGeneralizedDivergences 81
2.1 Error-inducedDistanceandRobustRegressionTechniques 82
2.2 RobustEstimation 84
2.3 Csisza´rDivergences 90
2.4 BregmanDivergence 96
2.4.1 BregmanMatrixDivergences 103
2.5 Alpha-Divergences 104
2.5.1 AsymmetricAlpha-Divergences 104
2.5.2 SymmetricAlpha-Divergences 110
2.6 Beta-Divergences 112
2.7 Gamma-Divergences 116
2.8 DivergencesDerivedfromTsallisandRe´nyiEntropy 118
2.8.1 ConcludingRemarks 119
Appendix2.A:InformationGeometry,CanonicalDivergence,andProjection 120
2.A.1 SpaceofProbabilityDistributions 120
2.A.2 GeometryofSpaceofPositiveMeasures 123
Appendix2.B:ProbabilityDensityFunctionsforVariousDistributions 125
References 127
3 MultiplicativeIterativeAlgorithmsforNMFwithSparsityConstraints 131
3.1 ExtendedISRAandEMMLAlgorithms:RegularizationandSparsity 132
3.1.1 MultiplicativeNMFAlgorithmsBasedontheSquaredEuclideanDistance 132
3.1.2 MultiplicativeNMFAlgorithmsBasedonKullback-LeiblerI-Divergence 139
3.2 MultiplicativeAlgorithmsBasedonAlpha-Divergence 143
3.2.1 MultiplicativeAlphaNMFAlgorithm 143
3.2.2 GeneralizedMultiplicativeAlphaNMFAlgorithms 147
3.3 AlternatingSMART:SimultaneousMultiplicativeAlgebraicReconstructionTechnique 148
3.3.1 AlphaSMARTAlgorithm 148
3.3.2 GeneralizedSMARTAlgorithms 150
Contents vii
3.4 MultiplicativeNMFAlgorithmsBasedonBeta-Divergence 151
3.4.1 MultiplicativeBetaNMFAlgorithm 151
3.4.2 MultiplicativeAlgorithmBasedontheItakura-SaitoDistance 156
3.4.3 GeneralizedMultiplicativeBetaAlgorithmforNMF 156
3.5 AlgorithmsforSemi-orthogonalNMFandOrthogonalThree-FactorNMF 157
3.6 MultiplicativeAlgorithmsforAffineNMF 159
3.7 MultiplicativeAlgorithmsforConvolutiveNMF 160
3.7.1 MultiplicativeAlgorithmforConvolutiveNMFBasedonAlpha-Divergence 162
3.7.2 MultiplicativeAlgorithmforConvolutiveNMFBasedonBeta-Divergence 162
3.7.3 EfficientImplementationofCNMFAlgorithm 165
3.8 SimulationExamplesforStandardNMF 166
3.9 ExamplesforAffineNMF 170
3.10 MusicAnalysisandDecompositionUsingConvolutiveNMF 176
3.11 DiscussionandConclusions 184
Appendix3.A:FastAlgorithmsforLarge-scaleData 187
3.A.1 RandomBlock-wiseProcessingApproach–Large-scaleNMF 187
3.A.2 Multi-layerProcedure 188
3.A.3 ParallelProcessing 188
Appendix3.B:PerformanceEvaluation 188
3.B.1 Signal-to-Interference-Ratio-SIR 188
3.B.2 PeakSignal-to-Noise-Ratio(PSNR) 190
Appendix3.C:ConvergenceAnalysisoftheMultiplicativeAlphaNMFAlgorithm 191
Appendix3.D:MATLABImplementationoftheMultiplicativeNMFAlgorithms 193
3.D.1 AlphaAlgorithm 193
3.D.2 SMARTAlgorithm 195
3.D.3 ISRAAlgorithmforNMF 197
Appendix3.E:AdditionalMATLABFunctions 198
3.E.1 Multi-layerNMF 198
3.E.2 MCAnalysiswithDistributedComputingTool 199
References 199
4 AlternatingLeastSquaresandRelatedAlgorithmsforNMFandSCAProblems 203
4.1 StandardALSAlgorithm 203
4.1.1 MultipleLinearRegression–VectorizedVersionofALSUpdateFormulas 206
4.1.2 WeightedALS 206
4.2 MethodsforImprovingPerformanceandConvergenceSpeedofALSAlgorithms 207
4.2.1 ALSAlgorithmforVeryLarge-scaleNMF 207
4.2.2 ALSAlgorithmwithLine-Search 208
4.2.3 AccelerationofALSAlgorithmviaSimpleRegularization 208
4.3 ALSAlgorithmwithFlexibleandGeneralizedRegularizationTerms 209
4.3.1 ALSwithTikhonovTypeRegularizationTerms 210
4.3.2 ALSAlgorithmswithSparsityControlandDecorrelation 211
4.4 CombinedGeneralizedRegularizedALSAlgorithms 212
4.5 Wang-HancewiczModifiedALSAlgorithm 213
4.6 ImplementationofRegularizedALSAlgorithmsforNMF 213
4.7 HALSAlgorithmanditsExtensions 214
4.7.1 ProjectedGradientLocalHierarchicalAlternatingLeastSquares(HALS)
Algorithm 214
4.7.2 ExtensionsandImplementationsoftheHALSAlgorithm 216
4.7.3 FastHALSNMFAlgorithmforLarge-scaleProblems 217
viii Contents
4.7.4 HALSNMFAlgorithmwithSparsity,SmoothnessandUncorrelatedness
Constraints 220
4.7.5 HALSAlgorithmforSparseComponentAnalysisandFlexible
ComponentAnalysis 222
4.7.6 SimplifiedHALSAlgorithmforDistributedandMulti-taskCompressedSensing 227
4.7.7 GeneralizedHALS-CSAlgorithm 231
4.7.8 GeneralizedHALSAlgorithmsUsingAlpha-Divergence 233
4.7.9 GeneralizedHALSAlgorithmsUsingBeta-Divergence 234
4.8 SimulationResults 236
4.8.1 UnderdeterminedBlindSourceSeparationExamples 236
4.8.2 NMFwithSparseness,OrthogonalityandSmoothnessConstraints 237
4.8.3 SimulationsforLarge-scaleNMF 239
4.8.4 IllustrativeExamplesforCompressedSensing 241
4.9 DiscussionandConclusions 249
Appendix4.A:MATLABSourceCodeforALSAlgorithm 252
Appendix4.B:MATLABSourceCodeforRegularizedALSAlgorithms 253
Appendix4.C:MATLABSourceCodeforMixedALS-HALSAlgorithms 256
Appendix4.D:MATLABSourceCodeforHALSCSAlgorithm 259
Appendix4.E:AdditionalMATLABFunctions 261
References 264
5 ProjectedGradientAlgorithms 267
5.1 ObliqueProjectedLandweber(OPL)Method 268
5.2 Lin’sProjectedGradient(LPG)AlgorithmwithArmijoRule 270
5.3 Barzilai-BorweinGradientProjectionforSparseReconstruction(GPSR-BB) 271
5.4 ProjectedSequentialSubspaceOptimization(PSESOP) 273
5.5 InteriorPointGradient(IPG)Algorithm 275
5.6 InteriorPointNewton(IPN)Algorithm 276
5.7 RegularizedMinimalResidualNormSteepestDescentAlgorithm(RMRNSD) 279
5.8 SequentialCoordinate-WiseAlgorithm(SCWA) 281
5.9 Simulations 283
5.10 Discussions 289
Appendix5.A:StoppingCriteria 290
Appendix5.B:MATLABSourceCodeforLin’sPGAlgorithm 292
References 293
6 Quasi-NewtonAlgorithmsforNonnegativeMatrixFactorization 295
6.1 ProjectedQuasi-NewtonOptimization 296
6.1.1 ProjectedQuasi-NewtonforFrobeniusNorm 296
6.1.2 ProjectedQuasi-NewtonforAlpha-Divergence 298
6.1.3 ProjectedQuasi-NewtonforBeta-Divergence 303
6.1.4 PracticalImplementation 305
6.2 GradientProjectionConjugateGradient 305
6.3 FNMAalgorithm 308
6.4 NMFwithQuadraticProgramming 310
6.4.1 NonlinearProgramming 311
6.4.2 QuadraticProgramming 312
6.4.3 Trust-regionSubproblem 314
6.4.4 UpdatesforA 316
6.5 HybridUpdates 318