迁移学习算法研究-庄福振New

迁移学习算法研究庄福振中国科学院计算技术研究所2016年4月18日TrainingDataClassifierUnseenData(…,long,T)good!Whatif…2传统监督机器学习(1/2)2023/2/1[fromProf.QiangYang]传统监督机器学习(2/2)32023/2/1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迁移学习42023/2/1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场景(1/4)52023/2/1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新闻新闻网页分类异构特征空间6Theappleisthepomaceousfruitoftheappletree,speciesMalusdomesticaintherosefamilyRosaceae...BananaisthecommonnameforatypeoffruitandalsotheherbaceousplantsofthegenusMusawhichproducethiscommonlyeatenfruit...Training:TextFuture:ImagesApplesBananas迁移学习场景(2/4)2023/2/1[fromProf.QiangYang]XinJin,FuzhenZhuang,SinnoJialinPan,ChangyingDu,PingLuo,QingHe:HeterogeneousMulti-taskSemanticFeatureLearningforClassification.CIKM2015:1847-1850.TestTestTrainingTrainingClassifierClassifier72.65%DVDElectronicsElectronics84.60%ElectronicsDrop!迁移学习场景(3/4)72023/2/1[fromProf.QiangYang]8DVDElectronicsBookKitchenClothesVideogameFruitHotelTeaImpractical!迁移学习场景(4/4)2023/2/1[fromProf.QiangYang]OutlineConceptLearningforTransferLearningConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningTransferLearningusingAuto-encodersTransferLearningfromMultipleSourceswithAutoencoderRegularizationSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders92023/2/1ConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningforTransferLearning102023/2/1IntroductionManytraditionallearningtechniquesworkwellonlyundertheassumption: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!11ConceptLearningforTransferLearning2023/2/1Motivation(1/3)ExampleAnalysis

Productannouncement:HP'sjust-releasedLaserJetProP1100printerandtheLaserJetProM1130andM1210multifunctionprinters,price…performance

...AnnouncementforLenovoThinkPad

ThinkCentre–price$150offLenovoK300desktopusingcouponcode...LenovoThinkPad

ThinkCentre–price$200offLenovoIdeaPadU450plaptopusing....theirperformanceHPnewsLenovonewsProductwordconceptLaserJet,printer,price,performanceThinkPad,ThinkCentre,price,performanceRelatedProductannouncementdocumentclass:12Sharesomecommonwords:announcement,price,performance…indicateConceptLearningforTransferLearning2023/2/1Motivation(2/3)ExampleAnalysis:

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

13ProductProductannouncementwordconceptindicatesTheassociationbetweenwordconceptsanddocumentclassesisdomain-independent

ConceptLearningforTransferLearning2023/2/1Motivation(3/3)14Furtherobservations:Differentdomainsmayusesamekeywordstoexpressthesameconcept(denotedasidenticalconcept)Differentdomainsmayalsousedifferentkeywordstoexpressthesameconcept(denotedasalikeconcept)Differentdomainsmayalsohavetheirowndistinctconcepts(denotedasdistinctconcept)TheidenticalandalikeconceptsareusedasthesharedconceptsforknowledgetransferWetrytomodelthesethreekindsofconceptssimultaneouslyfortransferlearningtextclassificationConceptLearningforTransferLearning2023/2/1PreliminaryKnowledgeBasicformulaofmatrixtri-factorization:wheretheinputXistheword-documentco-occurrencematrix

denotesconceptinformation,mayvaryindifferentdomainsFdenotesthedocumentclassificationinformation

indeedistheassociationbetweenwordconceptsanddocumentclasses,mayretainstablecrossdomainsGS15ConceptLearningforTransferLearning2023/2/1Previousmethod-MTrickinSDM2010(1/2)SketchmapofMTrick

SourcedomainXs

FsGsFtGtTargetdomainXtSKnowledgeTransfer16ConceptLearningforTransferLearning2023/2/1Consideringthealikeconcepts MTrick(2/2)OptimizationproblemforMTrickG0isthesupervisioninformationtheassociationSissharedasbridgetotransferknowledge17ConceptLearningforTransferLearningDualTransferLearning(Longetal.,SDM2012),consideringidenticalandalikeconcepts2023/2/1TriplexTransferLearning(TriTL)(1/5)Furtherdividethewordconceptsintothreekinds:

18F1,identicalconcepts;F2,alikeconcepts;F3,distinctconceptsInput:ssourcedomainXr(1≤r≤s)withlabelinformation,ttargetdomainXr(s+1≤r≤s+t)WeproposeTriplexTransferLearningframeworkbasedonmatrixtri-factorization(TriTLforshort)

2023/2/1ConceptLearningforTransferLearningF1,S1andS2

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

192023/2/1ConceptLearningforTransferLearningTriTL(3/5)Wedevelopanalternativelyiterativealgorithmtoderivethesolutionandtheoreticallyanalyzeitsconvergence 202023/2/1ConceptLearningforTransferLearningTriTL(4/5)Classificationontargetdomains When1≤r≤s,Grcontainsthelabelinformation,soweremainitunchangedduringtheiterations

whenxibelongstoclassj,thenGr(i,j)=1,elseGr(i,j)=0Aftertheiteration,weobtaintheoutputGr(s+1≤r≤s+t),thenwecanperformclassificationaccordingtoGr212023/2/1ConceptLearningforTransferLearningTriTL(5/5)AnalysisofAlgorithmConvergence Accordingtothemethodologyofconvergenceanalysisinthetwoworks[Leeetal.,NIPS’01]and[Dingetal.,KDD’06],thefollowingtheoremholds.Theorem(Convergence):Aftereachroundofcalculatingtheiterativeformulas,theobjectivefunctionintheoptimizationproblemwillconvergemonotonically.222023/2/1ConceptLearningforTransferLearning232023/2/1rec.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

ConceptLearningforTransferLearning242023/2/1rec.autosrec.motorcyclesrec.baseballrec.hockeysci.cryptsic.electronicssci.medsci.spacerec+sci-baseballcrypySourcedomainautosspaceTargetdomainFortheclassificationproblemwithonesourcedomainandonetargetdomain,wecanconstruct144()

problemsDataPreparation(2/3)Constructclassificationtasks(TraditionalTL)ConceptLearningforTransferLearning252023/2/1Constructnewtransferlearningproblemsrec.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)SourcedomainTargetdomainConceptLearningforTransferLearning262023/2/1ComparedAlgorithmsConceptLearningforTransferLearningTraditionallearningAlgorithmsSupervisedLearning:LogisticRegression(LR)[Davidetal.,00]SupportVectorMachine(SVM)[Joachims,ICML’99]Semi-supervisedLearning:TSVM[Joachims,ICML’99]TransferlearningMethods:CoCC[Daietal.,KDD’07],DTL[Longetal.,SDM’12]Classificationaccuracyisusedastheevaluationmeasure272023/2/1ExperimentalResults(1/3)ConceptLearningforTransferLearningSorttheproblemswiththeaccuracyofLRDegreeoftransferdifficultyeasierGenerally,thelowerofaccuracyofLRcanindicatethehardertotransfer,whilethehigheronesindicatetheeasiertotransferharder282023/2/1ExperimentalResults(2/3)ConceptLearningforTransferLearningComparisonsamongTriTL,DTL,MTrick,CoCC,TSVM,SVMandLRondatasetrecvs.sci(144problems)TriTLcanperformwelleventheaccuracyofLRislowerthan65%292023/2/1ExperimentalResults(3/3)ConceptLearningforTransferLearningResultsonnewtransferlearningproblems,weonlyselecttheproblems,whoseaccuraciesofLRarebetween(50%,55%](Onlyslightlybetterthanrandomclassification,thustheymightbemuchmoredifficult).Weobtain65problemsTriTLalsooutperformsallthebaselinesConclusionsExplicitlydefinethreekindsofwordconcepts,i.e.,identicalconcept,alikeconceptanddistinctconceptProposeageneraltransferlearningframeworkbasedonnonnegativematrixtri-factorization,whichsimultaneouslymodelthethreekindsofconcepts(TriTL)Extensiveexperimentsshowtheeffectivenessoftheproposedapproach,especiallywhenthedistinctconceptsmayexist302023/2/1ConceptLearningforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningConceptLearningforTransferLearning312023/2/1322023/2/1MotivationConceptLearningforTransferLearningProductannouncement: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

332023/2/1SomenotationsddocumentydocumentclasszwordconceptSomedefinitionse.g.,p(price|Product),p(LaserJet|Product,)wwordrdomaine.g,p(Product|Productannouncement)PreliminaryKnowledge(1/3)ConceptLearningforTransferLearning342023/2/1ConceptLearningforTransferLearningPreliminaryKnowledge(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)Alikeconcept352023/2/1DualPLSA

(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,denotedasHIDCConceptLearningforTransferLearning362023/2/1Identicalconceptp(w|za)p(za|y)AlikeconceptTheextensionandintensionaredomainindependentp(w|zb,r)p(zb|y)HIDC(1/3)Theextensionisdomaindependent,whiletheintensionisdomainindependentConceptLearningforTransferLearning372023/2/1Distinctconceptp(w|zc,r)p(zc|y,r)ThejointprobabilitiesofthesethreegraphicalmodelsHIDC(2/3)TheextensionandintensionarebothdomaindependentConceptLearningforTransferLearning382023/2/1Givens+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)ConceptLearningforTransferLearning392023/2/1UsetheEMalgorithmtoderivethesolutionsEStep:ModelSolution(1/4)ConceptLearningforTransferLearning402023/2/1M

Step：ModelSolution(2/4)ConceptLearningforTransferLearning412023/2/1Semi-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)ConceptLearningforTransferLearning422023/2/1ClassificationfortargetdomainsAfterweobtainthefinalsolutionsofp(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)ConceptLearningforTransferLearning432023/2/1BaselinesComparedAlgorithmsSupervisedLearning: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:classificationaccuracyConceptLearningforTransferLearning442023/2/1Resultsonnewtransferlearningproblems,weselecttheproblems,whoseaccuraciesofLRarehigherthan50%,then334problemsareobtainedExperimentalResults(1/5)ConceptLearningforTransferLearning452023/2/1Resultsonnewtransferlearningproblems,weselecttheproblems,whoseaccuraciesofLRarehigherthan50%,then334problemsareobtainedExperimentalResults(2/5)ConceptLearningforTransferLearning462023/2/1ExperimentalResults(3/5)ConceptLearningforTransferLearning472023/2/1Sourcedomain:S

(rec.autos,

sci.space)，Targetdomain:T(rec.sport.hockey,talk.politics.mideast)STSTDistinctconceptSTAlikeconceptExperimentalResults(4/5)ConceptLearningforTransferLearning482023/2/1ExperimentalResults(5/5)ConceptLearningforTransferLearningIndeed,theproposedprobabilisticmethodHIDCisalsobetterthanTriTLThismayduetothereasonthatthereismoreclearerprobabilisticexplanationofHIDCp1(z,y)=p2(z,y)orp1(z|y)=p2(z|y)whichisbetter?p(z|y)p(y)492023/2/1[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.ReferencesConceptLearningforTransferLearningOutlineConceptLearningforTransferLearningConceptLearningbasedonNon-negativeMatrixTri-factorizationforTransferLearningConceptLearningbasedonProbabilisticLatentSemanticAnalysisforTransferLearningTransferLearningusingAuto-encodersTransferLearningfromMultipleSourceswithAutoencoderRegularizationSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders502023/2/1TransferLearningfromMultipleSourceswithAutoencoderRegularization512023/2/1TransferLearningUsingAuto-encoders52Motivation(1/2)TransferlearningbasedonoriginalfeaturespacemayfailtoachievehighperformanceonTargetdomaindataWeconsidertheautoencodertechniquetocollaborativelyfindanewrepresentationofbothsourceandtargetdomaindataElectronicsVideoGames

Compact;easytooperate;verygoodpicture,excited

aboutthequality;lookssharp!Averygood

game!Itisactionpacked

andfullofexcitement.Iamverymuchhooked

onthisgame.522023/2/1TransferLearningUsingAuto-encodersPreviousmethodsoftentransferfromonesourcedomaintoonetargetdomainWeconsidertheconsensusregularizedframeworkforlearningfrommultiplesourcedomainsDVDBookKitchenElectronicsWeproposeatransferlearningframeworkofconsensusregularizationautoencoderstolearnfrommultiplesourcesMotivation(2/2)532023/2/1TransferLearningUsingAuto-encodersAutoencoderNeuralNetworkMinimizingthereconstructionerrortoderivethesolution:whereh,garenonlinearactivationfunction,e.g.,Sigmoidfunction,forencodinganddecoding542023/2/1TransferLearningUsingAuto-encodersConsensusMeasure-(1/3)Example:three-classclassificationproblem,threeclassifierspredictinstancesf1f2f3f1f2f3x1111x2333x3222x4231x5313x6123ConstraintSource1:D1Source2:D2Source3:D3552023/2/1TransferLearningUsingAuto-encodersConsensusMeasure-(2/3)Example:three-classclassificationproblem,predictiononinstancexMinimalentropy,MaximalConsensusMaximalentropy,MinimalConsensusEntropybasedConsensusMeasure(Luoetal.,CIKM’08)θiistheparametervectorofclassifieri,Cistheclasslabelset562023/2/1TransferLearningUsingAuto-encodersConsensusMeasure-(3/3)Forsimplicity,theconsensusmeasureforbinaryclassificationcanberewrittenasInthiswork,weimposetheconsensusregularizationtoautoencoders,andtrytoimprovethelearningperformancefrommultiplesourcedomainssincetheireffectsonmakingthepredictionconsensusaresimilar.572023/2/1TransferLearningUsingAuto-encodersSomeNotations

SourcedomainsGivenrsourcedomains:,i.e.,

,.

ThefirstcorrespondingdatamatrixisTargetdomainThecorrespondingdatamatrixis

Thegoalistotrainaclassifier

ftomakeprecisepredictionson.582023/2/1TransferLearningUsingAuto-encodersFrameworkofCRAThedatafromallsourceandtargetdomainssharethesameencodinganddecodingweightsTheclassifierstrainedfromthenewrepresentationareregularizedtopredictthesameresultsontargetdomaindata592023/2/1TransferLearningUsingAuto-encodersOptimizationProblemofCRATheoptimizationproblem:ReconstructionError602023/2/1TransferLearningUsingAuto-encodersOptimizationProblemofCRATheoptimizationproblem:ConsensusRegularization612023/2/1TransferLearningUsingAuto-encodersOptimizationProblemofCRATheoptimizationproblem:ThetotallossofsourceclassifiersoverthecorrespondingsourcedomaindatawiththehiddenrepresentationWeighdecayterm622023/2/1TransferLearningUsingAuto-encodersTheSolutionofCRAWeusethegradientdescentmethodtoderivethesolutionofallparametersƞisthelearningrate.ThetimecomplexityisO(rnmk)Theoutput:theencodinganddecodingparameters,andsourceclassifierswithlatentrepresentation.632023/2/1TransferLearningUsingAuto-encodersTargetClassifierConstructionTwoScheme:Trainthesourceclassifiersbasedonandcombinethemas,whereCombineallthesourcedomaindataasZSandtrainaunifiedclassifierusinganysupervisedlearningalgorithms,e.g.,SVM,LogisticRegression(LR).ThetwoaccuraciesaredenotedasCRAvandCRAu,respectively642023/2/1TransferLearningUsingAuto-encodersDataSets-(1/2)ImageData(fromLuoetal.,CIKM08)(Someexamples)AB

A1A2A3A4B1B2B3B4Threesources:A1B1A2B2A3B3Targetdomain:A4B4Totally,96()3-sourcevs1-targetdomain(3vs1)probleminstancescanbeconstructedfortheexperimentalevaluation652023/2/1TransferLearningUsingAuto-encodersDataSets-(2/2)SentimentClassification(fromBlitzeretal.,ACL07)Four3-sourcevs1-targetdomainclassificationproblemsareconstructedDVDBookKitchenElectronicsTheaccuracyontargetdomaindataisusedastheevaluationmeasureBothSVMandLRareusedtotrainclassifiersonthenewrepresentation662023/2/1TransferLearningUsingAuto-encodersAllComparedAlgorithmsBaselinesSupervisedlearningonoriginalfeatures: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.672023/2/1TransferLearningUsingAuto-encoders68ExperimentalResults-(1/2)TransferLearningwithMultipleSourcesviaConsensusRegularizationAutoencodersFuzhenZhuang,XiaohuCheng,SinnoJialinPan,WenchaoYu,QingHe,andZhongzhiShiResultson96imageclassificationproblems69ExperimentalResults-(2/2)TransferLearningwithMultipleSourcesviaConsensusRegularizationAutoencodersFuzhenZhuang,XiaohuCheng,SinnoJialinPan,WenchaoYu,QingHe,andZhongzhiShiResultson4sentimentclassificationproblemsConclusionsThewellknownrepresentationlearningtechniqueautoencoderisconsidered,andweformalizetheautoencodersandconsensusregularizationintoaunifiedoptimizationframeworkExtensivecomparisonexperimentsonimageandsentimentdataareconductedtoshowtheeffectivenessoftheproposealgorithm702023/2/1TransferLearningUsingAuto-encodersSupervisedRepresentationLearning:TransferLearningwithDeepAuto-encoders712023/2/1TransferLearningUsingAuto-encodersAutoencoderisanunsupervisedfeaturelearningalgorithm,whichcannoteffectivelymakeuseofthelabelinformationLimitationofBasicAutoencoderContributionofThisWorkWeextendAutoencodertomulti-layerstructure,andincorporatethelabelasonelayerMotivation722023/2/1TransferLearningUsingAuto-encoders源领域和目标领域共享编码和解码权重利用KL距离对隐层空间进行约束利用多类回归模型对类标层进行约束FrameworkofTLDA(1/5)732023/2/1TransferLearningUsingAuto-encoders目标是最小化重构误差：DeepAutoencoderFrameworkofTLDA(2/5)742023/2/1TransferLearningUsingAuto-encodersKL距离KL距离衡量的是两个概率分布的差异情况，计算公式如下：以上KL距离并不满足传