迁移学习算法研究课件

【5A文】迁移学习算法研究【5A文】迁移学习算法研究TrainingDataClassifierUnseenData(…,long,T)good!Whatif…2022/12/302传统监督机器学习(1/2)[fromProf.QiangYang]TrainingClassifierUnseenData(传统监督机器学习(2/2)2022/12/303传统监督学习同源、独立同分布两个基本假设标注足够多的训练样本在实际应用中通常不能满足！训练集测试集分类器训练集测试集分类器传统监督机器学习(2/2)2022/12/303传统监督学习迁移学习2022/12/304实际应用学习场景HP新闻Lenovo新闻不同源、分布不一致人工标记训练样本，费时耗力迁移学习运用已有的知识对不同但相关领域问题进行求解的一种新的机器学习方法放宽了传统机器学习的两个基本假设迁移学习2022/12/304实际应用学习场景HP新闻Le迁移学习场景(1/4)2022/12/305迁移学习场景无处不在迁移知识迁移知识图像分类HP新闻Lenovo新闻新闻网页分类迁移学习场景(1/4)2022/12/305迁移学习场景无处迁移学习场景(2/4)异构特征空间2022/12/306Theappleisthepomaceousfruitoftheappletree,speciesMalusdomesticaintherosefamilyRosaceae...BananaisthecommonnameforatypeoffruitandalsotheherbaceousplantsofthegenusMusawhichproducethiscommonlyeatenfruit...Training:TextFuture:ImagesApplesBananas[fromProf.QiangYang]XinJin,FuzhenZhuang,SinnoJialinPan,ChangyingDu,PingLuo,QingHe:HeterogeneousMulti-taskSemanticFeatureLearningforClassification.CIKM2015:1847-1850.迁移学习场景(2/4)异构特征空间2022/12/306ThTestTestTrainingTrainingClassifierClassifier72.65%DVDElectronicsElectronics84.60%ElectronicsDrop!迁移学习场景(3/4)2022/12/307[fromProf.QiangYang]TestTestTrain迁移学习场景(4/4)2022/12/308DVDElectronicsBookKitchenClothesVideogameFruitHotelTeaImpractical![fromProf.QiangYang]迁移学习场景(4/4)2022/12/308DVDElectOutlineConceptLearningforTransferLearningConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningTransferLearningusingAuto-encodersTransferLearningfromMultipleSourceswithAutoencoderRegularizationSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders2022/12/309OutlineConceptLearningforTrConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearning2022/12/30ConceptLearningforTransferLearning10ConceptLearningbasedonNon-Introduction2022/12/30ConceptLearningforTransferLearning11Manytraditionallearningtechniquesworkwellonlyundertheassumption:Trainingandtestdatafollowthesamedistribution

Training(labeled)ClassifierTest(unlabeled)FromdifferentcompaniesEnterpriseNewsClassification:includingtheclasses“ProductAnnouncement”,“Businessscandal”,“Acquisition”,……Productannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsDifferentdistributionFail!Introduction2022/12/30ConceptMotivation(1/3)2022/12/30ConceptLearningforTransferLearning12ExampleAnalysis

Productannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsProductwordconceptLaserJet,printer,price,performanceThinkPad,ThinkCentre,price,performanceRelatedProductannouncementdocumentclass:Sharesomecommonwords:announcement,price,performance…indicateMotivation(1/3)2022/12/30ConcMotivation(2/3)2022/12/30ConceptLearningforTransferLearning13ExampleAnalysis:

HPLaserJet,printer,price,performanceetal.LenovoThinkpad,Thinkcentre,price,performanceetal.Thewordsexpressingthesamewordconceptaredomain-dependent

ProductProductannouncementwordconceptindicatesTheassociationbetweenwordconceptsanddocumentclassesisdomain-independent

Motivation(2/3)2022/12/30ConcMotivation(3/3)2022/12/30ConceptLearningforTransferLearning14Furtherobservations:Differentdomainsmayusesamekeywordstoexpressthesameconcept(denotedasidenticalconcept)Differentdomainsmayalsousedifferentkeywordstoexpressthesameconcept(denotedasalikeconcept)Differentdomainsmayalsohavetheirowndistinctconcepts(denotedasdistinctconcept)TheidenticalandalikeconceptsareusedasthesharedconceptsforknowledgetransferWetrytomodelthesethreekindsofconceptssimultaneouslyfortransferlearningtextclassificationMotivation(3/3)2022/12/30ConcPreliminaryKnowledge2022/12/30ConceptLearningforTransferLearning15Basicformulaofmatrixtri-factorization:wheretheinputXistheword-documentco-occurrencematrix

denotesconceptinformation,mayvaryindifferentdomainsFdenotesthedocumentclassificationinformation

indeedistheassociationbetweenwordconceptsanddocumentclasses,mayretainstablecrossdomainsGSPreliminaryKnowledge2022/12/3Previousmethod-MTrickinSDM2010(1/2)2022/12/30ConceptLearningforTransferLearning16SketchmapofMTrick

SourcedomainXs

FsGsFtGtTargetdomainXtSKnowledgeTransferConsideringthealikeconcepts Previousmethod-MTrickinSDMTrick(2/2)OptimizationproblemforMTrick2022/12/30ConceptLearningforTransferLearning17G0isthesupervisioninformationtheassociationSissharedasbridgetotransferknowledgeDualTransferLearning(Longetal.,SDM2012),consideringidenticalandalikeconceptsMTrick(2/2)OptimizationproblTriplexTransferLearning(TriTL)(1/5)2022/12/30ConceptLearningforTransferLearning18Furtherdividethewordconceptsintothreekinds:

F1,identicalconcepts;F2,alikeconcepts;F3,distinctconceptsInput:ssourcedomainXr(1≤r≤s)withlabelinformation,ttargetdomainXr(s+1≤r≤s+t)WeproposeTriplexTransferLearningframeworkbasedonmatrixtri-factorization(TriTLforshort)

TriplexTransferLearning(TriF1,S1andS2

aresharedasthebridgeforknowledgetransferacrossdomainsThesupervisioninformationisintegratedbyGr(1≤r≤s)insourcedomainsTriTL(2/5)OptimizationProblem

2022/12/30ConceptLearningforTransferLearning19F1,S1andS2aresharedasthTriTL(3/5)Wedevelopanalternativelyiterativealgorithmtoderivethesolutionandtheoreticallyanalyzeitsconvergence 2022/12/30ConceptLearningforTransferLearning20TriTL(3/5)WedevelopanalterTriTL(4/5)Classificationontargetdomains When1≤r≤s,Grcontainsthelabelinformation,soweremainitunchangedduringtheiterationswhenxibelongstoclassj,thenGr(i,j)=1,elseGr(i,j)=0Aftertheiteration,weobtaintheoutputGr(s+1≤r≤s+t),thenwecanperformclassificationaccordingtoGr2022/12/30ConceptLearningforTransferLearning21TriTL(4/5)ClassificationontTriTL(5/5)AnalysisofAlgorithmConvergence Accordingtothemethodologyofconvergenceanalysisinthetwoworks[Leeetal.,NIPS’01]and[Dingetal.,KDD’06],thefollowingtheoremholds.Theorem(Convergence):Aftereachroundofcalculatingtheiterativeformulas,theobjectivefunctionintheoptimizationproblemwillconvergemonotonically.2022/12/30ConceptLearningforTransferLearning22TriTL(5/5)AnalysisofAlgorit2022/12/30ConceptLearningforTransferLearning23rec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacecomp.graphicscomp.sys.ibm.pc.hardwarecomp.sys.mac.hardwarecomp.windows.xtalk.politics.misctalk.politics.gunstalk.politics.mideasttalk.religion.miscrecscicomptalkDataPreparation(1/3)20Newsgroups Fourtopcategories,eachtopcategorycontainsfoursub-categories SentimentClassification,fourdomains:books,dvd,electronics,kitchenRandomlyselecttwodomainsassources,andtherestastargets,then6problemscanbeconstructed

2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning24rec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacerec+sci-baseballcrypySourcedomainautosspaceTargetdomainFortheclassificationproblemwithonesourcedomainandonetargetdomain,wecanconstruct144()

problemsDataPreparation(2/3)Constructclassificationtasks(TraditionalTL)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning25Constructnewtransferlearningproblemsrec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacerec+sci-baseballcrypyautosspacecomp.graphicscomp.sys.ibm.pc.hardwarecomp.sys.mac.hardwarecomp.windows.xtalk.politics.misctalk.politics.gunstalk.politics.mideasttalk.religion.misccomptalkautosgraphicsMoredistinctconceptsmayexist!DataPreparation(3/3)SourcedomainTargetdomain2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning26ComparedAlgorithmsTraditionallearningAlgorithmsSupervisedLearning:LogisticRegression(LR)[Davidetal.,00]SupportVectorMachine(SVM)[Joachims,ICML’99]Semi-supervisedLearning:TSVM[Joachims,ICML’99]TransferlearningMethods:CoCC[Daietal.,KDD’07],DTL[Longetal.,SDM’12]Classificationaccuracyisusedastheevaluationmeasure2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning27ExperimentalResults(1/3)SorttheproblemswiththeaccuracyofLRDegreeoftransferdifficultyeasierGenerally,thelowerofaccuracyofLRcanindicatethehardertotransfer,whilethehigheronesindicatetheeasiertotransferharder2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning28ExperimentalResults(2/3)ComparisonsamongTriTL,DTL,MTrick,CoCC,TSVM,SVMandLRondatasetrecvs.sci(144problems)TriTLcanperformwelleventheaccuracyofLRislowerthan65%2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning29ExperimentalResults(3/3)Resultsonnewtransferlearningproblems,weonlyselecttheproblems,whoseaccuraciesofLRarebetween(50%,55%](Onlyslightlybetterthanrandomclassification,thustheymightbemuchmoredifficult).Weobtain65problemsTriTLalsooutperformsallthebaselines2022/12/30ConceptLearningforConclusions2022/12/30ConceptLearningforTransferLearning30Explicitlydefinethreekindsofwordconcepts,i.e.,identicalconcept,alikeconceptanddistinctconceptProposeageneraltransferlearningframeworkbasedonnonnegativematrixtri-factorization,whichsimultaneouslymodelthethreekindsofconcepts(TriTL)Extensiveexperimentsshowtheeffectivenessoftheproposedapproach,especiallywhenthedistinctconceptsmayexistConclusions2022/12/30ConceptLConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearning2022/12/30ConceptLearningforTransferLearning31ConceptLearningbasedonProb2022/12/30ConceptLearningforTransferLearning32MotivationProductannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsProductwordconceptLaserJet,printer,price,performanceThinkPad,ThinkCentre,price,performanceRelatedProductannouncementdocumentclass:Sharesomecommonwords:announcement,price,performance…indicateRetrospecttheexample

2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning33SomenotationsddocumentydocumentclasszwordconceptSomedefinitionse.g.,p(price|Product),p(LaserJet|Product,)wwordrdomaine.g,p(Product|Productannouncement)PreliminaryKnowledge(1/3)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning34PreliminaryKnowledge(2/3)ProductLaserJet,printer,announcement,price,ThinkPad,ThinkCentre,announcement,priceProductannouncementp(w|z,r1)p(w|z,r2)p(z|y)p(w|z,r1)≠p(w|z,r2)E.g.,p(LaserJet|Product,HP)≠p(LaserJet|Product,Lenovo)p(z|y,r1)=p(z|y,r2)E.g.,p(Product|Productannoucement,HP)=p(Product|Productannoucement,Lenovo)Alikeconcept2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning35DualPLSA

(D-PLSA)Jointprobabilityoverallvariablesp(w,d)=p(w|z)p(z|y)p(d|y)p(y)GivendatadomainX,theproblemofmaximumloglikelihoodislogp(X;θ)=logΣz

p(Z,X;θ)

θ

includesalltheparametersp(w|z),p(z|y),p(d|y),p(y).Z

denotesallthelatentvariablesPreliminaryKnowledge(3/3)TheproposedtransferlearningalgorithmbasedonD-PLSA,denotedasHIDC2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning36Identicalconceptp(w|za)p(za|y)AlikeconceptTheextensionandintensionaredomainindependentp(w|zb,r)p(zb|y)HIDC(1/3)Theextensionisdomaindependent,whiletheintensionisdomainindependent2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning37Distinctconceptp(w|zc,r)p(zc|y,r)ThejointprobabilitiesofthesethreegraphicalmodelsHIDC(2/3)Theextensionandintensionarebothdomaindependent2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning38Givens+t

datadomainsX={X1,…,Xs,Xs+1,…,Xs+t},withoutlossofgenerality,thefirstsdomainsaresourcedomains,andthelefttdomainsaretargetdomainsConsiderthethreekindsofconcepts:TheLog

likelihoodfunctionislogp(X;θ)=logΣz

p(Z,X;θ)

θ

includesallparametersp(w|za),p(w|zb,r),p(w|zc,r),p(za|y),p(zb|y),p(zc|y,r),p(d|y,r),p(y|r),p(r).HIDC(3/3)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning39UsetheEMalgorithmtoderivethesolutionsEStep:ModelSolution(1/4)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning40M

Step：ModelSolution(2/4)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning41Semi-supervisedEMalgorithm:whenrisfromsourcedomains,thelabeledinformationp(d|y,r)isknownandp(y|r)

canbeinferedp(d|y,r)=1/ny,r,ifdbelongsyindomainr,ny,risthenumberofdocumentsinclassyindomainr,else

p(d|y,c)=0p(y|r)=ny,r/nr

,nr

isthenumberofdocumentsindomainr

whenrisfromsourcedomains,p(d|y,r)andp(y|r)keepunchangedduringtheiterations,whichsupervisetheoptimizingprocessModelSolution(3/4)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning42ClassificationfortargetdomainsAfterweobtainthefinalsolutionsofp(w|za),p(w|zb,r),p(w|zc,r),p(za|y),p(zb|y),p(zc|y,r),p(d|y,r),p(y|r),p(r)Wecancomputetheconditionalprobabilities:

ThenthefinalpredictionisDuringtheiterations,alldomainssharep(w|za),p(za|y),p(zb|y),

whichactasthebridgeforknowledgetransferModelSolution(4/4)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning43BaselinesComparedAlgorithmsSupervisedLearning:LogisticRegression(LG)[Davidetal.,00]SupportVectorMachine(SVM)[Joachims,ICML’99]Semi-supervisedLearning:TSVM[Joachims,ICML’99]TransferLearning:CoCC[Daietal.,KDD’07]CD-PLSA[Zhuangetal.,CIKM’10]DTL[Longetal.,SDM’12]OurMethodsHIDCMeasure:classificationaccuracy2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning44Resultsonnewtransferlearningproblems,weselecttheproblems,whoseaccuraciesofLRarehigherthan50%,then334problemsareobtainedExperimentalResults(1/5)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning45Resultsonnewtransferlearningproblems,weselecttheproblems,whoseaccuraciesofLRarehigherthan50%,then334problemsareobtainedExperimentalResults(2/5)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning46ExperimentalResults(3/5)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning47Sourcedomain:S

(rec.autos,

sci.space)，Targetdomain:T(rec.sport.hockey,talk.politics.mideast)STSTDistinctconceptSTAlikeconceptExperimentalResults(4/5)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning48ExperimentalResults(5/5)Indeed,theproposedprobabilisticmethodHIDCisalsobetterthanTriTLThismayduetothereasonthatthereismoreclearerprobabilisticexplanationofHIDCp1(z,y)=p2(z,y)orp1(z|y)=p2(z|y)whichisbetter?p(z|y)p(y)2022/12/30ConceptLearningfor2022/12/30ConceptLearningforTransferLearning49[1]FuzhenZhuang,PingLuo,HuiXiong,QingHe,YuhongXiong,ZhongzhiShi:ExploitingAssociationsbetweenWordClustersandDocumentClassesforCross-DomainTextCategorization.SDM2010,pp.13-24.[2]FuzhenZhuang,PingLuo,ZhiyongShen,QingHe,YuhongXiong,ZhongzhiShi,HuiXiong:CollaborativeDual-PLSA:miningdistinctionandcommonalityacrossmultipledomainsfortextclassification.CIKM2010,pp.359-368.[3]FuzhenZhuang,PingLuo,ZhiyongShen,QingHe,YuhongXiong,ZhongzhiShi,HuiXiong:MiningDistinctionandCommonalityacrossMultipleDomainsUsingGenerativeModelforTextClassification.IEEETrans.Knowl.DataEng.24(11):2025-2039(2012).[3]FuzhenZhuang,PingLuo,ChangyingDu,QingHe,ZhongzhiShi:Triplextransferlearning:exploitingbothsharedanddistinctconceptsfortextclassification.WSDM2013,pp.425-434.[4]FuzhenZhuang,PingLuo,PeifengYin,QingHe,ZhongzhiShi.:ConceptLearningforCross-domainTextClassification:aGeneralProbabilisticFramework.IJCAI2013,pp.1960-1966.References2022/12/30ConceptLearningforOutlineConceptLearningforTransferLearningConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningTransferLearningusingAuto-encodersTransferLearningfromMultipleSourceswithAutoencoderRegularizationSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders2022/12/3050OutlineConceptLearningforTrTransferLearningfromMultipleSourceswithAutoencoderRegularization2022/12/30TransferLearningUsingAuto-encoders51TransferLearningfromMultipl2022/12/3052Motivation(1/2)TransferlearningbasedonoriginalfeaturespacemayfailtoachievehighperformanceonTargetdomaindataWeconsidertheautoencodertechniquetocollaborativelyfindanewrepresentationofbothsourceandtargetdomaindataElectronicsVideoGames

Compact;easytooperate;verygoodpicture,excited

aboutthequality;lookssharp!Averygood

game!Itisactionpacked

andfullofexcitement.Iamverymuchhooked

onthisgame.52TransferLearningUsingAuto-encoders2022/12/3052Motivation(1/2)TraPreviousmethodsoftentransferfromonesourcedomaintoonetargetdomainWeconsidertheconsensusregularizedframeworkforlearningfrommultiplesourcedomainsDVDBookKitchenElectronicsWeproposeatransferlearningframeworkofconsensusregularizationautoencoderstolearnfrommultiplesourcesMotivation(2/2)2022/12/30TransferLearningUsingAuto-encoders53PreviousmethodsoftentransfeAutoencoderNeuralNetworkMinimizingthereconstructionerrortoderivethesolution:whereh,garenonlinearactivationfunction,e.g.,Sigmoidfunction,forencodinganddecoding2022/12/30TransferLearningUsingAuto-encoders54AutoencoderNeuralNetworkMConsensusMeasure-(1/3)Example:three-classclassificationproblem,threeclassifierspredictinstancesf1f2f3f1f2f3x1111x2333x3222x4231x5313x61232022/12/30TransferLearningUsingAuto-encoders55ConstraintSource1:D1Source2:D2Source3:D3ConsensusMeasure-(1/3)ExamConsensusMeasure-(2/3)Example:three-classclassificationproblem,predictiononinstancex2022/12/30TransferLearningUsingAuto-encoders56Minimalentropy,MaximalConsensusMaximalentropy,MinimalConsensusEntropybasedConsensusMeasure(Luoetal.,CIKM’08)θiistheparametervectorofclassifieri,CistheclasslabelsetConsensusMeasure-(2/3)ExamConsensusMeasure-(3/3)Forsimplicity,theconsensusmeasureforbinaryclassificationcanberewrittenasInthiswork,weimposetheconsensusregularizationtoautoencoders,andtrytoimprovethelearningperformancefrommultiplesourcedomainssincetheireffectsonmakingthepredictionconsensusaresimilar.2022/12/30TransferLearningUsingAuto-encoders57ConsensusMeasure-(3/3)ForSomeNotationsSourcedomainsGivenrsourcedomains:,i.e.,

,.ThefirstcorrespondingdatamatrixisTargetdomainThecorrespondingdatamatrixisThegoalistotrainaclassifier

ftomakeprecisepredictionson.2022/12/30TransferLearningUsingAuto-encoders58SomeNotationsSourcedomaiFrameworkofCRAThedatafromallsourceandtargetdomainssharethesameencodinganddecodingweightsTheclassifierstrainedfromthenewrepresentationareregularizedtopredictthesameresultsontargetdomaindata2022/12/30TransferLearningUsingAuto-encoders59FrameworkofCRAThedatafrOptimizationProblemofCRATheoptimizationproblem:ReconstructionError2022/12/30TransferLearningUsingAuto-encoders60OptimizationProblemofCRAOptimizationProblemofCRATheoptimizationproblem:ConsensusRegularization2022/12/30TransferLearningUsingAuto-encoders61OptimizationProblemofCRAOptimizationProblemofCRATheoptimizationproblem:ThetotallossofsourceclassifiersoverthecorrespondingsourcedomaindatawiththehiddenrepresentationWeighdecayterm2022/12/30TransferLearningUsingAuto-encoders62OptimizationProblemofCRATheSolutionofCRAWeusethegradientdescentmethodtoderivethesolutionofallparametersƞisthelearningrate.ThetimecomplexityisO(rnmk)Theoutput:theencodinganddecodingparameters,andsourceclassifierswithlatentrepresentation.2022/12/30TransferLearningUsingAuto-encoders63TheSolutionofCRAWeusetheTargetClassifierConstructionTwoScheme:Trainthesourceclassifiersbasedonandcombinethemas,whereCombineallthesourcedomaindataasZSandtrainaunifiedclassifierusinganysupervisedlearningalgorithms,e.g.,SVM,LogisticRegression(LR).ThetwoaccuraciesaredenotedasCRAvandCRAu,respectively2022/12/30TransferLearningUsingAuto-encoders64TargetClassifierConstructionDataSets-(1/2)ImageData(fromLuoetal.,CIKM08)(Someexamples)AB

A1A2A3A4B1B2B3B4Threesources:A1B1A2B2A3B3Targetdomain:A4B4Totally,96()3-sourcevs1-targetdomain(3vs1)probleminstancescanbeconstructedfortheexperimentalevaluation2022/12/30TransferLearningUsingAuto-encoders65DataSets-(1/2)ImageData(DataSets-(2/2)SentimentClassification(fromBlitzeretal.,ACL07)Four3-sourcevs1-targetdomainclassificationproblemsareconstructedDVDBookKitchenElectronicsTheaccuracyontargetdomaindataisusedastheevaluationmeasureBothSVMandLRareusedtotrainclassifiersonthenewrepresentation2022/12/30TransferLearningUsingAuto-encoders66DataSets-(2/2)SentimentClassAllComparedAlgorithmsBaselinesSupervisedlearningonoriginalfeatures:SVM[Joachims,ICML’99],LogisticRegression(LR)[Davidetal.,00]Embeddingmethodbasedonautoencoders(EAER)[Yuetal.,ECML’13]MarginalizedStackedDenoisingAutoencoders

(mSDA)[Chenetal.,ICML’12]TransferComponentAnalysis(TCA)[Panetal.,TNN’11]Transferlearningfrommultiplesources(CCR3)(Luoetal.,CIKM’08)Ourmethod:CRAvandCRAuForthemethodswhichcannothandlemultiplesources,wetraintheclassifiersfromeachsourcedomainandmergeddataofallsources(r+1accuracies).Finally,maximal,meanandminimalvaluesarereported.2022/12/30TransferLearningUsingAuto-encoders67AllComparedAlgorithmsBaselinTransferLearningwithMultipleSourcesviaConsensusRegularizationAutoencodersFuzhenZhuang,XiaohuCheng,SinnoJialinPan,WenchaoYu,QingHe,andZhongzhiShi68ExperimentalResults-(1/2)Resultson96imageclassificationproblemsTransferLearningwithMultiplTransferLearningwithMultipleSourcesviaConsensusRegularizationAutoencodersFuzhenZhuang,XiaohuCheng,SinnoJialinPan,WenchaoYu,QingHe,andZhongzhiShi69ExperimentalResults-(2/2)Resultson4sentimentclassificationproblemsTransferLearningwithMultiplConclusionsThewellknownrepresentationlearningtechniqueautoencoderisconsidered,andweformalizetheautoencodersandconsensusregularizationintoaunifiedoptimizationframeworkExtensivecomparisonexperimentsonimageandsentimentdataareconductedtoshowtheeffectivenessoftheproposealgorithm2022/12/30TransferLearningUsingAuto-encoders70ConclusionsThewellknownrSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders2022/12/30TransferLearningUsingAuto-encoders71SupervisedRepresentationLearAutoencoderisanunsupervisedfeaturelearningalgorithm,whichcannoteffectivelymakeuseofthelabelinformationLimitationofBasicAutoencoderContributionofThisWorkWeextendAutoencodertomulti-layerstructure,andincorporatethelabelasonelayerMotivation2022/12/30TransferLearnin