Fast Multilevel CVT-Based Adaptive Data Visualization Algorithm

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摘要 Efficientdatavisualizationtechniquesarecriticalformanyscientificapplications.CentroidalVoronoitessellation(CVT)basedalgorithmsofferaconvenientvehicleforperformingimageanalysis,segmentationandcompressionwhileallowingtooptimizeretainedimagequalitywithrespecttoagivenmetric.InexperimentalsciencewithdatacountsfollowingPoissondistributions,severalCVT-baseddatatessellationalgorithmshavebeenrecentlydeveloped.Althoughtheysurpasstheirpredecessorsinrobustnessandqualityofreconstructeddata,timeconsumptionremainstobeanissueduetoheavyutilizationoftheslowlyconvergingLloyditeration.Thispaperdiscussesonepossibleapproachtoacceleratingdatavisualizationalgorithms.ItreliesonamultidimensionalgeneralizationoftheoptimizationbasedmultilevelalgorithmforthenumericalcomputationoftheCVTsintroducedin[1],wherearigorousproofofitsuniformconvergencehasbeenpresentedin1-dimensionalsetting.Themultidimensionalimplementationemploysbarycentriccoordinatebasedinterpolationandmaximalindependentsetcoarseningprocedures.Itisshownthatwhencoupledwithbinaccretionalgorithmaccountingforthediscretenatureofthedata,thealgorithmoutperformsLloyd-basedschemesandpreservesuniformconvergencewithrespecttotheproblemsize.Althoughnumericaldemonstrationsprovidedarelimitedtospectroscopydataanalysis,themethodhasacontext-independentsetupandcanpotentiallydeliversignificantspeeduptootherscientificandengineeringapplications.
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出版日期 2010年02月12日(中国期刊网平台首次上网日期,不代表论文的发表时间)
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