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1、TheSupervisedIBP:NeighbourhoodPreservingIn niteLatentFeatureModelsNoviQuadriantoViktoriiaSharmanskaUniversityofCambridgeISTAustriaCambridge,UKKlosterneuburg,Austrianovi.quadrianto@eng.cam.ac.ukviktoriia.sharmanska@ist.ac.atDavidA.KnowlesZoubinGhahramaniStanfordUniversityUniversityofCambr
2、idgeStanford,CA,USCambridge,UKknowles84@gmail.comzoubin@eng.cam.ac.ukAbstractInmanycases,anaturalrepresentationofanobjectwouldalloweachobjecttoadmitmultiplelatentfea-Weproposeaprobabilisticmodeltoinfertures.Classicalstatisticaltechniquesrequirethenum-supervisedlatentvariablesintheHamming
3、beroflatentfeaturestobe xedapriori.Recently,spacefromobserveddata.Ourmodelal-nonparametricBayesianmodelshaveemergedasanlowssimultaneousinferenceofthenumberelegantapproachtodealwiththisissuebyallowingofbinarylatentvariables,andtheirvalues.thenumberoffeaturestobeinferredfromdata.OneThelate
4、ntvariablespreserveneighbourhoodclassofthesemodelsutilisetheIndianBu etProcessstructureofthedatainasensethatobjects(IBP)prior(Griths&Ghahramani,2005)toallowinthesamesemanticconcepthavesimilaranunboundednumberoffeatures.AlmostallIBP-latentvalues,andobjectsindi erentcon-basedstatisticalmo
5、delsaregearedtowardsunsuper-ceptshavedissimilarlatentvalues.Wefor-visedlatentfeaturelearning.Whileunsupervisedla-mulatethesupervisedin nitelatentvariabletentfeaturemodelsarepromising,forexample,asanproblembasedonanintuitiveprincipleofexploratorytoolfordiscoveringcompacthiddenstruc-pullin
6、gobjectstogetheriftheyareoftheturesinobserveddata,inmanypracticalsettingswesametype,andpushingthemapartiftheyseeksupervisedlatentvariables,thataresemanticallyarenot.Wethencombinethisprinciplewithmeaningfulandencodesupervisedsideinformation.a
exibleIndianBu etProcesspriorontheSuchsupervis
7、edsideinformationcanbeexpressedaslatentvariables.Weshowthattheinferredneighbours(similar)andnon-neighbours(dissimilar)supervisedlatentvariablescanbedirectlydatapairs,asin(Schultz&Joachims,2003)forex-usedtoperformanearestneighboursearchample,andcanbeusedforretrievalofseman
