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《[NIPS 2012] Multilabel Classification using Bayesian Compressed Sensing》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、MultilabelClassificationusingBayesianCompressedSensingAshishKapoory,PrateekJainzandRaajayViswanathanzyMicrosoftResearch,Redmond,USAzMicrosoftResearch,Bangalore,INDIAfakapoor,prajain,t-rviswag@microsoft.comAbstractInthispaper,wepresentaBayesianframeworkformultilabelclassifica
2、tionusingcompressedsensing.Thekeyideaincompressedsensingformultilabelclassi-ficationistofirstprojectthelabelvectortoalowerdimensionalspaceusingarandomtransformationandthenlearnregressionfunctionsovertheseprojections.Ourapproachconsidersbothofthesecomponentsinasingleprobabili
3、sticmodel,therebyjointlyoptimizingovercompressionaswellaslearningtasks.Wethenderiveanefficientvariationalinferenceschemethatprovidesjointposteriordistri-butionoveralltheunobservedlabels.Thetwokeybenefitsofthemodelarethata)itcannaturallyhandledatasetsthathavemissinglabelsandb
4、)itcanalsomeasureuncertaintyinprediction.Theuncertaintyestimateprovidedbythemodelallowsforactivelearningparadigmswhereanoracleprovidesinformationaboutlabelsthatpromisetobemaximallyinformativeforthepredictiontask.Ourexperimentsshowsignificantboostoverpriormethodsintermsofpre
5、dictionperformanceoverbenchmarkdatasets,bothinthefullylabeledandthemissinglabelscase.Finally,wealsohighlightvarioususefulactivelearningscenariosthatareenabledbytheprobabilisticmodel.1IntroductionLargescalemultilabelclassificationproblemsariseinseveralpracticalapplicationsan
6、dhasrecentlygeneratedalotofinterestwithseveralefficientalgorithmsbeingproposedfordifferentsettings[1,2].Aprimaryreasonforthrustinthisareaisduetoexplosionofweb-basedapplications,suchasPicasa,Facebookandotheronlinesharingsites,thatcanobtainmultipletagsperdatapoint.Forexample,
7、usersonthewebcanannotatevideosandimageswithseveralpossiblelabels.Suchapplicationshaveprovidedanewdimensiontotheproblemastheseapplicationstypicallyhavemillionsoftags.Mostoftheexistingmultilabelmethodslearnadecisionfunctionorweightvectorperlabelandthencombinethedecisionfunct
8、ionsinacertainmannertopredictlabelsforanunseenpoint[3,4,2,5,6].However,suchapproachesquic