data fusion techniques and applicationyunshengb.com/wp-content/uploads/2017/12/... ·...
TRANSCRIPT
![Page 1: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/1.jpg)
DataFusionTechniquesandApplication
GuangyuZhou
Referencepaper:ZhengYu:MethodologiesforCross-DomainDataFusion:AnOverview
![Page 2: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/2.jpg)
Agenda§ Introduction§ Relatedwork§ Datafusiontechniques&applications
§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ Summary
![Page 3: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/3.jpg)
Whatisdatafusion?§ Datafusion istheprocessofintegratingmultipledatasourcestoproducemoreconsistent,accurate,andusefulinformationthanthatprovidedbyanyindividualdatasource---- Wikipedia
![Page 4: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/4.jpg)
Whydatafusion?§ Inthebigdataera,wefaceadiversityofdatasetsfromdifferentsourcesindifferentdomains,consistingofmultiplemodalities:§ Representation,distribution,scale,anddensity.
§ Howtounlockthepowerofknowledgefrommultipledisparate(butpotentiallyconnected)datasets?§ Treatingdifferentdatasetsequallyorsimplyconcatenatingthefeaturesfromdisparatedatasets?
![Page 5: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/5.jpg)
Whydatafusion?§ Inthebigdataera,wefaceadiversityofdatasetsfromdifferentsourcesindifferentdomains,consistingofmultiplemodalities:§ Representation,distribution,scale,anddensity.
§ Howtounlockthepowerofknowledgefrommultipledisparate(butpotentiallyconnected)datasets?§ Treatingdifferentdatasetsequallyorsimplyconcatenatingthefeaturesfromdisparatedatasets
§ Useadvanceddatafusiontechniquesthatcanfuseknowledgefromvariousdatasetsorganicallyinamachinelearninganddataminingtask
![Page 6: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/6.jpg)
RelatedWork§ RelationtoTraditionalDataIntegration
![Page 7: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/7.jpg)
RelatedWork§ RelationtoHeterogeneousInformationNetwork
§ Itonlylinkstheobjectinasingledomain:§ Bibliographicnetwork,author,papers,andconferences.§ Flickrinformationnetwork:users,images,tags,andcomments.
§ Aimtofusedataacrossdifferentdomains:§ Trafficdata,socialmediaandairquality
§ Heterogeneousnetworkmaynotbeabletofindexplicitlinkswithsemanticmeaningsbetweenobjectsofdifferentdomains.
![Page 8: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/8.jpg)
Datafusionmethodologies§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ multi-viewlearning-based§ similarity-based§ probabilisticdependency-based§ andtransferlearning-basedmethods.
![Page 9: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/9.jpg)
Stage-baseddatafusionmethods§ Differentdatasetsatdifferentstagesofadataminingtask.§ Datasetsarelooselycoupled,withoutanyrequirementsontheconsistencyoftheirmodalities.
§ Canbeameta-approachusedtogetherwithotherdatafusionmethods
![Page 10: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/10.jpg)
Mappartitionandgraphbuildingfortaxitrajectory
![Page 11: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/11.jpg)
Friendrecommendation
§ Stages§ I.Detectstaypoints§ II.MaptoPOIvector§ III.Hierarchicalclustering§ IV.Partialtree§ V.Hierarchicalgraph
§ ->comparable(fromsametree)
![Page 12: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/12.jpg)
Datafusionmethodologies§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ multi-viewlearning-based§ similarity-based§ probabilisticdependency-based§ andtransferlearning-basedmethods.
![Page 13: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/13.jpg)
Feature-level-baseddatafusion§ DirectConcatenation
§ Treatfeaturesextractedfromdifferentdatasetsequally,concatenatingthemsequentiallyintoafeaturevector
§ Limitations:§ Over-fitting inthecaseofasmallsizetrainingsample,andthespecificstatisticalpropertyofeachviewisignored.
§ Difficulttodiscoverhighlynon-linearrelationshipsthatexistbetweenlow-levelfeaturesacrossdifferentmodalities.
§ Redundanciesanddependenciesbetweenfeaturesextractedfromdifferentdatasetswhichmaybecorrelated.
![Page 14: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/14.jpg)
Feature-level-baseddatafusion§ DirectConcatenation+sparsityregularization:
§ handlethefeatureredundancyproblem
§ Dualregularization(i.e.,zero-meanGaussianplusinverse-gamma)§ RegularizemostfeatureweightstobezeroorclosetozeroviaaBayesiansparseprior
§ Allowforthepossibilityofamodellearninglargeweightsforsignificantfeatures
![Page 15: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/15.jpg)
Feature-level-baseddatafusion§ DNN-BasedDataFusion§ Usingsupervised,unsupervisedandsemi-supervisedapproaches,DeepLearninglearnsmultiplelevelsofrepresentationandabstraction
§ Unifiedfeaturerepresentationfromdisparatedataset
![Page 16: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/16.jpg)
DNN-BasedDataFusion§ DeepAutoencoderModelsoffeaturerepresentationbetween2modalities(audio+video)
![Page 17: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/17.jpg)
MultimodalDeepBoltzmannMachine§ ThemultimodalDBMisagenerativeandundirectedgraphicmodel.
§ Enablesbi-directionalsearch.
§ Tolearn
![Page 18: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/18.jpg)
LimitationsofDNN-basedfusionmodel§ Performanceheavilydependonparameters
§ Findingoptimalparametersisalaborintensiveandtime-consumingprocessgivenalargenumberofparametersandanon-convexoptimizationsetting.
§ Hardtoexplainwhatthemiddle-levelfeaturerepresentationstandsfor.§ WedonotreallyunderstandthewayaDNNmakesrawfeaturesabetterrepresentationeither.
![Page 19: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/19.jpg)
Semanticmeaning-baseddatafusion§ Unlikefeature-basedfusion,semanticmeaning-basedmethodsunderstandtheinsight ofeachdatasetandrelations betweenfeaturesacrossdifferentdatasets.
§ 4groupsofsemanticmeaningmethods:§ multi-view-based,similarity-based,probabilisticdependency-based,andtransfer-learning-basedmethods.
![Page 20: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/20.jpg)
Datafusionmethodologies§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ multi-viewlearning-based§ co-training,multiplekernellearning(MKL),subspacelearning
§ similarity-based§ probabilisticdependency-based§ andtransferlearning-basedmethods.
![Page 21: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/21.jpg)
Multi-ViewBasedDataFusion§ Differentdatasetsordifferentfeaturesubsetsaboutanobjectcanberegardedasdifferentviewsontheobject.
§ Person:face,fingerprint,orsignature§ Image:colorortexturefeatures
§ Latentconsensus&complementaryknowledge§ 3subcategories:
§ 1)co-training§ 2)multiplekernellearning(MKL)§ 3)subspacelearning
![Page 22: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/22.jpg)
Multi-ViewBasedDataFusion:Co-training§ Co-trainingconsidersasettinginwhicheachexamplecanbepartitionedintotwodistinctviews,makingthreemainassumptions:§ Sufficiency:eachviewissufficientforclassificationonitsown§ Compatibility:thetargetfunctionsinbothviewspredictthesamelabelsforco-occurringfeatureswithhighprobability
§ Conditionalindependence:theviewsareconditionallyindependentgiventheclasslabel.(Toostronginpractice)
![Page 23: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/23.jpg)
Multi-ViewBasedDataFusion:Co-training§ OriginalCo-training
![Page 24: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/24.jpg)
Co-training-basedairqualityinferencemodel
![Page 25: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/25.jpg)
Multi-ViewBasedDataFusion:MKL§ 2.Multi-KernelLearning§ Akernelisahypothesisonthedata§ MKL referstoasetofmachinelearningmethodsthatusesapredefinedsetofkernelsandlearnsanoptimallinearornon-linearcombinationofkernelsaspartofthealgorithm.§ Eg:Ensembleandboostingmethods,suchasRandomForest,areinspiredbyMKL.
![Page 26: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/26.jpg)
Multi-ViewBasedDataFusion:MKL§ MKL-basedframeworkforforecastingairquality.
![Page 27: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/27.jpg)
Multi-ViewBasedDataFusion:MKL§ TheMKL-basedframeworkoutperformsasinglekernel-basedmodelintheairqualityforecastexample§ Featurespace:
§ Thefeaturesusedbythespatialandtemporalpredictorsdonothaveanyoverlaps,providingdifferentviewsonastation’sairquality.
§ Model:§ Thespatialandtemporalpredictorsmodelthelocalfactorsandglobalfactorsrespectively,whichhavesignificantlydifferentproperties.
§ Parameterlearning:§ Decomposingabigmodelinto3coupledsmallonesscalesdowntheparameterspacestremendously.
![Page 28: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/28.jpg)
Multi-ViewBasedDataFusion:subspacelearning§ Obtainalatentsubspacesharedbymultipleviewsbyassumingthatinputviewsaregeneratedfromthislatentsubspace,
§ Subsequenttasks,suchasclassificationandclustering§ Lowerdimensionality
![Page 29: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/29.jpg)
Multi-ViewBasedDataFusion:subspacelearning§ Eg:PCA->
§ Linearcase:Canonicalcorrelationanalysis(CCA)§ maximizingthecorrelationbetween2viewsinthesubspace
§ Non-linear:KernelvariantofCCA(KCCA)§ mapeach(non-linear)datapointtoahigherspaceinwhichlinearCCAoperates.
![Page 30: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/30.jpg)
Multi-ViewBasedDataFusion§ SummaryofMulti-ViewBasedmethods
§ 1)co-training:maximizethemutualagreementontwodistinctviewsofthedata.
§ 2)multiplekernellearning(MKL):exploitkernelsthatnaturallycorrespondtodifferentviewsandcombinekernelseitherlinearlyornon-linearlytoimprovelearning.
§ 3)subspacelearning:obtainalatentsubspacesharedbymultipleviews,assumingthattheinputviewsaregeneratedfromthislatentsubspace
![Page 31: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/31.jpg)
Datafusionmethodologies§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ multi-viewlearning-based§ similarity-based
§ CoupledMatrixFactorization§ ManifoldAlignment
§ probabilisticdependency-based§ andtransferlearning-basedmethods.
![Page 32: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/32.jpg)
§ Recall:MatrixdecompositionbySVD
§ Problemsofsinglematrixdecompositionondifferentdatasets:§ Inaccuratecomplementationofmissingvaluesinthematrix.
![Page 33: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/33.jpg)
Similarity-Based:CoupledMatrixFactorization§ Solutionbycoupled(context-aware)matrixfactorization:
§ Toaccommodatedifferentdatasetswithdifferentmatrices(distribution,meaning),whichshareacommondimensionbetweenoneanother.
§ Bydecomposingthesematricescollaboratively,wecantransferthesimilaritybetweendifferentobjectslearnedfromadatasettoanotherone,thereforecomplementingthemissingvaluesmoreaccurately.
![Page 34: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/34.jpg)
CoupledMatrixFactorizationApplication§ Estimatethetravelspeedoneachroadsegmentinanentirecity,basedontheGPStrajectoryofasampleofvehicles
![Page 35: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/35.jpg)
CoupledMatrixFactorizationApplication§ Coupledmatrixfactorization
§ Objectivefunction:
![Page 36: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/36.jpg)
Similarity-Based:ManifoldAlignment§ Utilizestherelationshipsofinstanceswithineachdatasettostrengthentheknowledgeoftherelationships between thedatasets,therebyultimatelymapping initiallydisparatedatasetsto ajointlatentspace
§ Mapstwodatasets(X,Y)toanewjointlatentspace(f(X);g(Y)),
![Page 37: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/37.jpg)
Similarity-Based:ManifoldAlignment§ Preserves2similarities:
§ Thelocalsimilaritywithinadataset,
§ Thecorrespondencesacrossdifferentdatasets.
§ C,costfunction;F,embeddingofdata;W,similaritymatrix;a,theathdataset
![Page 38: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/38.jpg)
Similarity-Based:ManifoldAlignment§ Manifoldalignmentassumesthedisparatedatasetstobealignedhavethesameunderlyingmanifoldstructure
§ ThesecondlossfunctionissimplythelossfunctionforLaplacianEigen-mapsusingthejointadjacencymatrix:L=D- W
![Page 39: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/39.jpg)
CoupledMatrixFactorization+manifold§ Example:Inferthefine-grainednoisesituationbyusingcomplaintdatatogetherwithsocialmedia,roadnetworkdata,andPOIs
![Page 40: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/40.jpg)
Datafusionmethodologies§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ multi-viewlearning-based§ similarity-based§ probabilisticdependency-based§ andtransferlearning-basedmethods.
![Page 41: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/41.jpg)
ProbabilisticDependency-BasedFusion§ Thiscategoryofapproachesbridgesthegapbetweendifferentdatasetsbytheprobabilisticdependency,whichemphasizemoreabouttheinteraction ratherthanthesimilarity betweentwoobjects.
§ Twobranchesofgraphicalrepresentationsofdistributionsarecommonlyused:§ BayesianNetworks§ MarkovNetworks(a.k.a.MarkovRandomField)
![Page 42: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/42.jpg)
ProbabilisticDependency-BasedFusionModel§ ThegraphicalstructureoftrafficvolumeinferencemodelbasedonPOIs,roadnetworks,travelspeedandweather.§ Agraynodedenotesahiddenvariableandwhitenodesareobservations.§ 𝜃:roadhiddenvariable§ 𝛼:POIhiddenvariable§ 𝑁$:Trafficvolumehiddenvariable
![Page 43: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/43.jpg)
Datafusionmethodologies§ Stage-basedmethods§ Featurelevel-basedmethods§ Semanticmeaning-baseddatafusionmethods
§ multi-viewlearning-based§ similarity-based§ probabilisticdependency-based§ transferlearning-basedmethods.
![Page 44: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/44.jpg)
Transferlearning-basedmethods§ Anassumptioninmanymachinelearningalgorithmsisthatthetrainingandtestdatamustbeinthesamefeaturespace andhavethesamedistribution.
§ Transferlearning,incontrast,allowsthedomains,tasks,anddistributionsusedintrainingandtestingtobedifferent.
§ Examples:§ Auser’stransactionrecordsinAmazon->applicationoftravelrecommendation.
§ Theknowledgelearnedfromonecity’strafficdata->anothercity.
![Page 45: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/45.jpg)
TaxonomyofTransferlearning
![Page 46: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/46.jpg)
TransferbetweentheSameTypeofDatasets§ Examplesofmulti-tasktransferlearning
![Page 47: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/47.jpg)
TransferLearningamongMultipleDatasets
![Page 48: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/48.jpg)
ComparisonofDifferentDataFusionMethods
FillingMissingValues(ofasparsedataset),PredictFuture,CausalityInference,ObjectProfiling,andAnomalyDetection.
![Page 49: Data Fusion Techniques and Applicationyunshengb.com/wp-content/uploads/2017/12/... · Feature-level-based data fusion § Direct Concatenation § Treat features extracted from different](https://reader033.vdocuments.mx/reader033/viewer/2022050315/5f77b8643ee1c87ac56feac2/html5/thumbnails/49.jpg)
Thankyou!
Q&A