JMLR Workshop and Conference Proceedings vol Randomized partition trees for exact nearest neighbor search Sanjoy Dasgupta dasguptacs - Pdf

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DasguptaSinhatree,whichtakesO(n)spaceandanswersquerieswithanapproximationfactorc=1+inO((6=)dlogn)time(Aryaetal.,1998).Insomeoftheseresults,anexponentialdependenceondimensionisevident,andindeedthisisafamiliarblotonthenearestneighborlandscape.Onewaytomitigatethecurseofdimensionalityistoconsidersituationsinwhichdatahavelowintrinsicdimensiondo,eveniftheyhappentolieinRdforddoorinageneralmetricspace.Acommonassumptionisthatthedataaredrawnfromadoublingmeasureofdimensiondo(orequivalently,haveexpansionrate2do);thisisdenedinSection4.1below.Underthiscondition,KargerandRuhl(2002)haveaschemethatgivesexactanswerstonearestneighborqueriesintimeO(23dologn),usingadatastructureofsizeO(23don).Themorerecentcovertreealgorithm(Beygelzimeretal.,2006),whichhasbeenusedquitewidely,createsadatastructureinspaceO(n)andanswersqueriesintimeO(2dologn).Thereisalsoworkthatcombinesintrinsicdimensionandapproximatesearch.Thenavigatingnet(KrauthgamerandLee,2004),givendatafromametricspaceofdoublingdimensiondo,hassizeO(2O(do)n)andgivesa(1+)-approximateanswertoqueriesintimeO(2O(do)logn+(1=)O(do));thecrucialadvantagehereisthatdoublingdimensionisamoregeneralandrobustnotionthandoublingmeasure.Despitetheseandmanyotherresults,therearetwosignicantdecienciesinthenearestneighborliteraturethathavemotivatedthepresentpaper.First,existinganalyseshavesucceededatidentifying,foragivendatastructure,highlyspecicfamiliesofdataforwhichecientexactNNsearchispossible|forinstance,datafromdoublingmeasures|buthavefailedtoprovideamoregeneralcharacterization.Second,thereremainsaclassofnearestneighbordatastructuresthatarepopularandsuccessfulinpractice,butthathavenotbeenanalyzedthoroughly.Thesestructurescombineclassicalk-dtreepartitioningwithrandomizationandoverlappingcells,andarethesubjectofthispaper.1.1.ThreerandomizedtreestructuresforexactNNsearchThek-dtreeisapartitionofRdintohyper-rectangularcells,basedonasetofdatapoints(Bentley,1975).Therootofthetreeisasinglecellcorrespondingtotheentirespace.Acoordinatedirectionischosen,andthecellissplitatthemedianofthedataalongthisdirection(Figure1,left).Theprocessisthenrecursedonthetwonewlycreatedcells,andcontinuesuntilallleafcellscontainatmostsomepredeterminednumbernoofpoints.Whentherearendatapoints,thedepthofthetreeisatmostaboutlog(n=no).Givenak-dtreebuiltfromdatapointsS,thereareseveralwaystoansweranearestneighborqueryq.Thequickestanddirtiestoftheseistomoveqdownthetreetoitsappropriateleafcell,andthenreturnthenearestneighborinthatcell.ThisdefeatistsearchtakestimejustO(no+log(n=no)),whichisO(logn)forconstantno.Theproblemisthatq'snearestneighbormaywelllieinadierentcell,forinstancewhenthedatahappentobeconcentratednearcellboundaries.Consequently,thefailureprobabilityofthisschemecanbeunacceptablyhigh.Overtheyears,somesimpletrickshaveemerged,fromvarioussources,forreducingthefailureprobability.ThesearenicelylaidoutbyLiuetal.(2004),whoshowexperimentallythattheresultingalgorithmsareeectiveinpractice.2 DasguptaSinha Figure3:Threetypesofsplit.Thefractionsrefertoprobabilitymass.issomeconstant,whileischosenuniformlyatrandomfrom[1=4;3=4].unitsphere.Butnow,threesplitpointsarenoted:themedianm(C)ofthedataalongdirectionU,the(1=2)�fractilevaluel(C),andthe(1=2)+fractilevaluer(C).Hereisasmallconstant,like0:05or0:1.Theideaistosimultaneouslyentertainamediansplitleft=fx:x
Um(C)gright=fx:x
Um(C)gandanoverlappingsplit(withthemiddle2fractionofthedatafallingonbothsides)left=fx:x
Ur(C)gright=fx:x
Ul(C)g:Inthespilltree(Liuetal.,2004),eachdatapointinSisstoredinmultipleleaves,byfollowingtheoverlappingsplits.Aqueryisthenanswereddefeatist-style,byroutingittoasingleleafusingmediansplits.BoththeRPtreeandthespilltreehavequerytimesofO(no+log(n=no)),butthelattercanbeexpectedtohavealowerfailureprobability,andwewillseethisintheboundsweobtain.Ontheotherhand,theRPtreerequiresjustlinearspace,whilethesizeofthespilltreeisO(n1=(1�lg(1+2))).When=0:05,forinstance,thesizeisO(n1:159).Inviewofthesetradeos,weconsiderafurthervariant,whichwecallthevirtualspilltree.Itstoreseachdatapointinasingleleaf,followingmediansplits,andhencehaslinearsize.However,eachqueryisroutedtomultipleleaves,usingoverlappingsplits,andthereturnvalueisitsnearestneighborintheunionoftheseleaves.ThevarioussplitsaresummarizedinFigure3,andthethreetreesusethemasfollows: RoutingdataRoutingqueries RPtree PerturbedsplitPerturbedsplitSpilltree OverlappingsplitMediansplitVirtualspilltree MediansplitOverlappingsplitOnesmalltechnicality:if,forinstance,thereareduplicatesamongthedatapoints,itmightnotbepossibletoachieveamediansplit,orasplitatadesiredfractile.Wewillignorethesediscretizationproblems.4 DasguptaSinha2.ApotentialfunctionforpointcongurationsTomotivatethepotentialfunction,westartbyconsideringwhathappenswhentherearejusttwodatapointsandonequerypoint.2.1.HowrandomprojectionaectstherelativeplacementofthreepointsConsideranyq;x;y2Rd,suchthatxisclosertoqthanisy;thatis,kq�xkkq�yk.NowsupposethatarandomdirectionUischosenfromtheunitsphereSd�1,andthatthepointsareprojectedontothisdirection.Whatistheprobabilitythatyfallsbetweenqandxonthisline?Thefollowinglemmaanswersthisquestionexactly.Anapproximatesolution,withdierentproofmethod,wasgivenearlierbyKleinberg(1997).Lemma1Pickanyq;x;y2Rdwithkq�xkkq�yk.PickarandomunitdirectionU.Theprobability,overU,thaty
Ufalls(strictly)betweenq
Uandx
Uis1 arcsin0@kq�xk kq�yks 1�(q�x)
(y�x) kq�xkky�xk21A:ProofWemayassumeUisdrawnfromN(0;Id),thed-dimensionalGaussianwithmeanzeroandunitcovariance.ThisgivestherightdistributionifwescaleUtounitlength,butwecanskipthislaststepsinceithasnoeectonthequestionathand.Wecanalsoassume,withoutlossofgenerality,thatqliesattheoriginandthatxliesalongthe(positive)x1-axis:thatis,q=0andx=kxke1.ItwillthenbehelpfultosplitthedirectionUintotwopieces,itscomponentU1inthex1-direction,andtheremainingd�1coordinatesUR.Likewise,wewillwritey=(y1;yR).IfyR=0thenx,y,andqarecollinear,andtheprojectionofycannotpossiblyfallbetweenthoseofxandq.HenceforthassumeyR6=0.LetEdenotetheeventofinterest:Ey
Ufallsbetweenq
U(thatis,0)andx
U(thatis,kxkU1)yR
URfallsbetween�y1U1and(kxk�y1)U1Theintervalofinterestiseither(�y1jU1j;(kxk�y1)jU1j),ifU10,or(�(kxk�y1)jU1j;y1jU1j),ifU10.NowyR
URisindependentofU1andisdistributedasN(0;kyRk2),whichissymmetricandthusassignsthesameprobabilitymasstothetwointervals.ThereforePrU(E)=PrU1PrUR(�y1jU1jyR
UR(kxk�y1)jU1j):LetZandZ0beindependentstandardnormalsN(0;1).SinceU1isdistributedasZandyR
URisdistributedaskyRkZ0,PrU(E)=Pr(�y1jZjkyRkZ0(kxk�y1)jZj)=PrZ0 jZj2�y1 kyRk;kxk�y1 kyRk:6 DasguptaSinhaInthetreedatastructuresweanalyze,mostcellscontainonlyasubsetofthedatafx1;:::;xng.Foracellthatcontainsmofthesepoints,theappropriatevariantofism(q;fx1;:::;xng)=1 mmXi=2kq�x(1)k kq�x(i)k:Corollary4Pickanypointsq;x1;:::;xnandletSdenoteanysubsetofthexithatincludesx(1).IfqandthepointsinSareprojectedtoadirectionUchosenatrandomfromtheunitsphere,thenforany01,theprobability(overU)thatatleastanfractionoftheprojectedSfallsbetweenqandx(1)isupper-boundedby(1=2)jSj(q;fx1;:::;xng).ProofApplyTheorem3toS,notingthatthecorrespondingvalueofismaximizedwhenSconsistsofthepointsclosesttoq;andthenapplyMarkov'sinequality. 2.3.ExtensiontoknearestneighborsIfweareinterestedinndingtheknearestneighbors,asuitablegeneralizationofmisk;m(q;fx1;:::;xng)=1 mmXi=k+1(kq�x(1)k+


+kq�x(k)k)=k kq�x(i)k:Theorem5Pickanypointsq;x1;:::;xnandletSdenoteasubsetofthexithatincludesx(1);:::;x(k).SupposeqandthepointsinSareprojectedtoarandomunitdirectionU.Then,forany(k�1)=jSj1,theprobability(overU)thatintheprojection,thereissome1jkforwhichmpointsliebetweenx(j)andqisatmostk 2(�(k�1)=jSj)k;jSj(q;fx1;:::;xng):Thistheorem,andmanyoftheothersthatfollow,areprovedintheappendix.3.RandomizedpartitiontreesWe'llnowseethatthefailureprobabilityoftherandomprojectiontreeisproportionaltoln(1=),whilethatofthetwospilltreesisproportionalto.Westartwiththesecondresult,sinceitisthemorestraightforwardofthetwo.3.1.RandomizedspilltreesInarandomizedspilltree,eachcellissplitalongadirectionchosenuniformlyatrandomfromtheunitsphere.Twokindsofsplitsaresimultaneouslyconsidered:(1)asplitatthemedian(alongtherandomdirection),and(2)anoverlappingsplitwithonepartcontainingthebottom1=2+fractionofthecell'spoints,andtheotherpartcontainingthetop1=2+fraction,where01=2(recallFigure3).8 RandomizedtreesforNNsearch3.3.Couldcoordinatedirectionsbeused?Thetreedatastructureswehavestudiedmakecrucialuseofrandomprojectionforsplittingcells.Itwouldnotsucetousecoordinatedirections,asink-dtrees.Toseethis,considerasimpleexample.Letq,thequerypoint,betheorigin,andsupposethedatapointsx1;:::;xn2Rdarechosenasfollows:x1istheall-onesvector.Eachxi;i�1,ischosenbypickingacoordinateatrandom,settingitsvaluetoM,andthensettingallremainingcoordinatestouniform-randomnumbersintherange(0;1).HereMissomeverylargeconstant.ForlargeenoughM,thenearestneighborofqisx1.BylettingMgrowfurther,wecanlet(q;fx1;:::;xng)getarbitrarilyclosetozero,whichmeansthattherandomprojectionmethodswillworkwell.However,anycoordinateprojectionwillcreateadisastrouslylargeseparationbetweenqandx1:onaverage,a(1�1=d)fractionofthedatapointswillfallbetweenthem.4.BoundingTheexactnearestneighborschemesweanalyzehaveerrorprobabilitiesrelatedto,whichliesintherange[0;1].Theworstcaseiswhenallpointsareequidistant,inwhichcaseisexactly1,butthisisapathologicalsituation.Isitpossibletoboundundersimpleassumptionsonthedata?Inthissectionwestudytwosuchassumptions.Ineachcase,querypointsarearbitrary,butthedataareassumedtohavebeendrawni.i.d.fromanunderlyingdistribution.4.1.DatadrawnfromadoublingmeasureSupposethedatapointsaredrawnfromadistributiononRdwhichisadoublingmeasure:thatis,thereexistaconstantC�0andasubsetXRdsuchthat(B(x;2r))C
(B(x;r))forallx2Xandallr�0:HereB(x;r)istheclosedEuclideanballofradiusrcenteredatx.Tounderstandthiscondition,itishelpfultoalsolookatanalternativeformulationthatisessentiallyequivalent:thereexistaconstantdo�0andasubsetXRdsuchthatforallx2X,allr�0,andall1,wehave(B(x;r))do
(B(x;r)).Inotherwords,theprobabilitymassofaballgrowspolynomiallyintheradius.Comparingthistothestandardformulaforthevolumeofaball,weseethatthedegreeofthispolynomial,do(=log2C),canreasonablybethoughtofasthe\dimension"ofmeasure.Theorem8SupposeiscontinuousonRdandisadoublingmeasureofdimensiondo2.Pickanyq2Xanddrawx1;:::;xn.Pickany01=2.Withprobability1�311 RandomizedtreesforNNsearchTheoveralldistributionisthusamixture=w11+


+wttwhosejthcomponentisaBernoulliproductdistributionj=B(p(j)1)


B(p(j)N).HereB(p)isashorthandforthedistributiononf0;1gwithexpectedvaluep.Itwillsimplifythingstoassumethat0p(j)i1=2;thisisnotahugeassumptionif,say,stopwordshavebeenremoved.Forthepurposesofbounding,weareinterestedinthedistributionofdH(q;X),whereXischosenfromanddHdenotesHammingdistance.Thisisasumofsmallindependentquantities,anditiscustomarytoapproximatesuchsumsbyaPoissondistribution.Inthecurrentcontext,however,thisapproximationisratherpoor,andweinsteadusecountingargumentstodirectlyboundhowrapidlythedistributiongrows.Theresultsstandinstarkcontrasttothoseweobtainedfordoublingmeasures,andrevealthistobeasubstantiallymoredicultsettingfornearestneighborsearch.Foradoublingmeasure,theprobabilitymassofaballB(q;r)doubleswheneverrismultipliedbyaconstant.Inourpresentsetting,itdoubleswheneverrisincreasedbyanadditiveconstant:Theorem10Supposethatallp(j)i2(0;1=2).LetLj=Pip(j)idenotetheexpectednumberofwordsinadocumentfromtopicj,andletL=min(L1;:::;Lt).Pickanyqueryq2f0;1gN,anddrawX.Forany`0,Pr(dH(q;X)=`+1) Pr(dH(q;X)=`)L�`=2 `+1:Now,xaparticularqueryq2f0;1gN,anddrawx1;:::;xnfromdistribution.Lemma11Thereisanabsoluteconstantcoforwhichthefollowingholds.Pickany01andanyk1,andletvdenotethesmallestintegerforwhichPrX(dH(q;X)v)(8=n)max(k;ln1=).Thenwithprobabilityatleast1�3overthechoiceofx1;:::;xn,foranymn,k;m(q;fx1;:::;xng)4r v coL�log2(n=m):Theimplicationofthislemmaisthatforanyofthethreetreedatastructures,thefailureprobabilityatasinglelevelisroughlyp v=L.ThismeansthatthetreecanonlybegrowntodepthO(p L=v),andthusthequerytimeisdominatedbyno=n
2�O(p L=v).Whennislarge,weexpectvtobesmall,andthusthequerytimeimprovesoverexhaustivesearchbyafactorofroughly2�p L.AcknowledgmentsWethanktheNationalScienceFoundationforsupportundergrantIIS-1162581.ReferencesN.AilonandB.Chazelle.ThefastJohnson-Lindenstrausstransformandapproximatenearestneighbors.SIAMJournalonComputing,39:302{322,2009.13 RandomizedtreesforNNsearchAppendixB.ProofofTheorem7Consideranyinternalnodeofthetreethatcontainsqaswellasmofthedatapoints,includingx(1).Whatistheprobabilitythatthesplitatthatnodeseparatesqfromx(1)?Toanalyzethis,letFdenotethefractionofthempointsthatfallbetweenqandx(1)alongtherandomly-chosensplitdirection.Sincethesplitpointischosenatrandomfromanintervalofmass1=2,theprobabilitythatitseparatesqfromx(1)isatmostF=(1=2).IntegratingoutF,wegetPr(qisseparatedfromx(1))Z10Pr(F=f)f 1=2df=2Z10Pr(F�f)df2Z10min1;m 2fdf=2Zm=20df+2Z1m=2m 2fdf=mln2e m;wherethesecondinequalityusesCorollary4.Thelemmafollowsbytakingaunionboundoverthepaththatconveysqfromroottoleaf,inwhichthenumberofdatapointsperlevelshrinksgeometrically,byafactorof3=4orbetter.Thesamereasoninggeneralizestoknearestneighbors.Thistime,Fisdenedtobethefractionofthempointsthatliebetweenqandthefurthestofx(1);:::;x(k)alongtherandomsplittingdirection.ThenqisseparatedfromoneoftheseneighborsonlyifthesplitpointliesinanintervalofmassFoneithersideofq,aneventthatoccurswithprobabilityatmost2F=(1=2).UsingTheorem5,Pr(qisseparatedfromsomex(j),1jk)Z10Pr(F=f)2f 1=2df=4Z10Pr(F�f)df4Z10min1;kk;m 2(f�(k�1)=m)df4Z(kk;m=2)+(k�1)=m0df+4Z1(kk;m=2)+(k�1)=mkk;m 2(f�(k�1)=m)df2kk;mln2e kk;m+4(k�1) m;andasbefore,wesumthisoveraroot-to-leafpathinthetree.AppendixC.ProofofTheorem8C.1.Thek=1caseWewillconsideracollectionofballsBo;B1;B2;:::centeredatq,withgeometricallyin-creasingradiiro;r1;r2;:::,respectively.Fori1,wewilltakeri=2iro.Thusbythedoublingcondition,(Bi)Ci(Bo),whereC=2do4.15 RandomizedtreesforNNsearchwherethelastinequalitycomesfrom(*).Tolower-bound2`,weagainuse(*)togetC`m=(2n(Bo)),whereupon2`m 2n(Bo)1=log2C=m 2ln(1=)1=log2Candwe'redone.C.2.Thek�1caseTheonlybigchangeisinthedenitionofro;itisnowtheradiusforwhich(Bo)=4 nmaxk;ln1 :Thus,whenx1;:::;xnaredrawnindependentlyatrandomfrom,theexpectednumberofthemthatfallinBoisatleast4k,andbyamultiplicativeChernoboundisatleastkwithprobability1�.TheballsB1;B2;:::aredenedasbefore,andonceagain,wecanconcludethatwithprobability1�2,eachBicontainsatmost2nCi(Bo)ofthedatapoints.Anypointx(i)62BoliesinsomeannulusBjnBj�1,anditscontributiontothesummationink;mis(kq�x(1)k+


+kq�x(k)k)=k kq�x(i)k1 2j�1:Therelationship(*)andtheremainderoftheargumentareexactlyasbefore.AppendixD.AusefultechnicallemmaLemma12SupposethatforsomeconstantsA;B�0anddo1,F(m)AB m1=doforallmno.Pickany01anddene`=log1=(n=no).Then:`Xi=0F(in)Ado 1�B no1=doand,ifnoB(A=2)do,`Xi=0F(in)ln2e F(in)Ado 1�B no1=do1 1�ln1 +ln2e A+1 dolnno B:17 RandomizedtreesforNNsearchTounderstandthisdistribution,westartwithageneralresultaboutsumsofBernoullirandomvariables.Noticethattheresultisexactlycorrectinthesituationwhereallpi=1=2.Lemma13SupposeZ1;:::;ZNareindependent,whereZi2f0;1gisaBernoullirandomvariablewithmean0ai1,anda1a2


aN.LetZ=Z1+


+ZN.Thenforany`0,Pr(Z=`+1) Pr(Z=`)1 `+1NXi=`+1ai 1�ai:ProofDeneri=ai=(1�ai)2(0;1);thenr1r2


rN.Now,forany`0,Pr(Z=`)=Xfi1;:::;i`g[N]ai1ai2


ai`Yj62fi1;:::;i`g(1�aj)=NYi=1(1�ai)Xfi1;:::;i`g[N]ai1 1�ai1ai2 1�ai2


ai` 1�ai`=NYi=1(1�ai)Xfi1;:::;i`g[N]ri1ri2


ri`wherethesummationsareoversubsetsfi1;:::;i`gof`distinctelementsof[N].Inthenalline,theproductofthe(1�ai)doesnotdependupon`andcanbeignored.Let'sfocusonthesummation;callitS`.WewouldliketocompareittoS`+1.S`+1isthesumof�N`+1
distinctterms,eachtheproductof`+1ri's.ThesetermsalsoappearinthequantityS`(r1+


+rN);infact,eachtermofS`+1appearsmultipletimes,`+1timestobeprecise.TheremainingtermsinS`(r1+


+rN)eachcontain`�1uniqueelementsandoneduplicatedelement.Byaccountinginthisway,wegetS`(r1+


+rN)=(`+1)S`+1+Xfi1;:::;i`g[N]ri1ri2


ri`(ri1+


+ri`)(`+1)S`+1+S`(r1+


+r`)sincetheri'sarearrangedindecreasingorder.HencePr(Z=`+1) Pr(Z=`)=S`+1 S`1 `+1(r`+1+


+rN);asclaimed. WenowapplythisresultdirectlytothesumofBernoullivariablesZ=dH(q;X).Lemma14Supposethatp1;:::;pN2(0;1=2).Pickanyqueryq2f0;1gN,anddrawXfromdistribution=B(p1)


B(pN).Thenforany`0,Pr(dH(q;X)=`+1) Pr(dH(q;X)=`)L�`=2 `+1;whereL=PipiistheexpectednumberofwordsinX.19 RandomizedtreesforNNsearchSupposethatforsomei�k,pointx(i)isatHammingdistance`fromq,thatis,x(i)2S`.Then(kq�x(1)k+


+kq�x(k)k)=k kq�x(i)kr v `sinceEuclideandistanceisthesquarerootofHammingdistance.Inboundingk;m,weneedtogaugetherangeofHammingdistancesspannedbyx(k+1);:::;x(m).Thegeometricgrowthrateofpart(c)impliesthatmostpointslieatHammingdistancecoLorgreaterfromq.ItalsomeansthatdH(q;x(m))�coL�log2(n=m).Thus,k;m(q;fx1;:::;xng)=1 mX�ik(kq�x(1)k+


+kq�x(k)k)=k kq�x(i)k1 mX`vjS`\fx(1);:::;x(m)gjr v `4r v coL�log2(n=m)wherethelaststepfollowsbylower-boundingjS`jbyanincreasinggeometricseries.21