遷移學習算法研究-莊福振New

遷移學習算法研究莊福振中國科學院計算技術研究所2016年4月18日TrainingDataClassifierUnseenData(…,long,T)good!Whatif…2傳統(tǒng)監(jiān)督機器學習(1/2)2023/2/1[fromProf.QiangYang]傳統(tǒng)監(jiān)督機器學習(2/2)32023/2/1傳統(tǒng)監(jiān)督學習同源、獨立同分布兩個基本假設標注足夠多的訓練樣本在實際應用中通常不能滿足！訓練集測試集分類器訓練集測試集分類器遷移學習42023/2/1實際應用學習場景HP新聞Lenovo新聞不同源、分布不一致人工標記訓練樣本，費時耗力遷移學習運用已有的知識對不同但相關領域問題進行求解的一種新的機器學習方法放寬了傳統(tǒng)機器學習的兩個基本假設遷移學習場景(1/4)52023/2/1遷移學習場景無處不在遷移知識遷移知識圖像分類HP新聞Lenovo新聞新聞網(wǎng)頁分類異構特征空間6Theappleisthepomaceousfruitoftheappletree,speciesMalusdomesticaintherosefamilyRosaceae...BananaisthecommonnameforatypeoffruitandalsotheherbaceousplantsofthegenusMusawhichproducethiscommonlyeatenfruit...Training:TextFuture:ImagesApplesBananas遷移學習場景(2/4)2023/2/1[fromProf.QiangYang]XinJin,FuzhenZhuang,SinnoJialinPan,ChangyingDu,PingLuo,QingHe:HeterogeneousMulti-taskSemanticFeatureLearningforClassification.CIKM2015:1847-1850.TestTestTrainingTrainingClassifierClassifier72.65%DVDElectronicsElectronics84.60%ElectronicsDrop!遷移學習場景(3/4)72023/2/1[fromProf.QiangYang]8DVDElectronicsBookKitchenClothesVideogameFruitHotelTeaImpractical!遷移學習場景(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-encodersTheSolutionofCRAWeusethegradientdescentmethodtoderivethesolutionofallparameters?isthelearningrate.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源領域和目標領域共享編碼和解碼權重利用KL距離對隱層空間進行約束利用多類回歸模型對類標層進行約束FrameworkofTLDA(1/5)732023/2/1TransferLearningUsingAuto-encoders目標是最小化重構誤差：DeepAutoencoderFrameworkofTLDA(2/5)742023/2/1TransferLearningUsingAuto-encodersKL距離KL距離衡量的是兩個概率分布的差異情況，計算公式如下：以上KL距離并不滿足傳