简介：Alotof3Dshapedescriptorsfor3Dshaperetrievalhavebeenpresentedsofar.Thispaperproposesanewmechanism,whichemploysseveralexistingglobalandlocal3Dshapedescriptorsasinput.Withthesparsetheory,somedescriptorswhichplaythemostimportantroleinmeasuringsimilaritybetweenquerymodelandthemodelinthedatasetareselectedautomaticallyandanaffinitymatrixisconstructed.Spectralclusteringmethodcanbeimplementedtothisaffinitymatrix.Spectralembeddingofthisaffinitymatrixcanbeappliedtoretrieval,whichintegratingalmostalltheadvantagesofselecteddescriptors.Inordertoverifytheperformanceofourapproach,weperformexperimentalcomparisonsonPrincetonShapeBenchmarkdatabase.Testresultsshowthatourmethodisapose-oblivious,efficientandrobustnessmethodforeithercompleteorincompletemodels.
简介：Privacypreservingdataminingalgorithmsarecrucialforthepersonaldataanalysis,suchasmedicalandfinancialrecords.Thispaperfocusesonfeatureselectionandproposesanewprivacypreservingdistributedalgorithm,whichcaneffectivelyselectfeaturesbasedondifferentialprivacyandGiniindexundertheMapReduceframework.Atthesametime,thetheoreticanalysisforprivacyguaranteeisalsopresented.Someexperimentsareconductedonbench-markdatasets,thesimulationresultsindicatethatduringtheselectionofimportantfeatures,theproposedalgorithmcanpreserveprivacyinformationtoacertainextentwithlesstimecostthanoncentralizedcounterpart.
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