Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

263 Pages·2012·12.1 MB·English

by  Masashi Sugiyama

MachinLee arniinnNg o n-StatiEonnvairryo nments
AdaptCiovmep utaatnidoM na chiLneea rning
ThomDaise ttEedriitcohr,
ChristBoipshheDorap v,Hi edc kerMmiacnh,Ja oerld aannMd,i chKaeealr ns,
AssocEidaitteo rs
A compleltieso tft heb ookpsu blisihnet dh isse riceasnb ef ounadt t heb ack
oft heb ook.
MACHINEL EARNINGI NN ON-STATIONAERNVYI RONMENTS
IntrodutcoCt oivoanr iSahtiAefd ta ptation
MasasShuig iyaamnadM otoaKkaiw anabe
TheM ITP ress
CambridMgaes,s achusetts
LondoEnn,g land
© 2012M assachusIentsttsi otfuT teec hnology
Allr ighrtess ervNeod p.a rotf t hibso okm ayb er eproduicnea dn yf ormb ya nye lectronic
orm echanimceaaln s (incplhuodtioncgo pyriencgo,r dionrig n,f ormatsitoonr aagned r etrieval)
withopuetr missiinwo rni tifrnogm thpeu blisher.
Fori nformaatbioounst p eciqaula ntdiitsyc oupnltesa,es mea il
[email protected]
Thibso okw ass eitn S ynteaxn dT imesRombaynN ewgen.
Printaenddb ounidn t heU niteSdt atoefsA merica.
LibraorfyC ongreCsast aloging-in-PDuabtlai cation
SugiyamMaa,s ash1i9,7 4-
Machinlee arninign n on-statieonnvairryo nm:ei nnttsr oducttoci oovna risahtieaft d aptat/i on
MasasShuig iyaarnnad M otoakKia wanabe.
p. cm.- (Adaptciovmep utatainodmn a chinlee arninsge ries)
Includbeisb liograrpehfiecraelna cnedis n dex.
ISBN9 78-0-262-01(7h0a9r-d1c oavlekrp:.a per)
I.M achinlee arningI..K awanabMeo,t oakiI.I T.i tle.
Q325.5.S824051 2
006.3'I-dc23
2011032824
10987653421
Contents
Foreworxdi
Prefacxei ii
INTRODUCTION
1 IntroducatnidPo rno blFeomr mulati3o n
1.1 MachinLee arniunngd eCro variSahtief t3
1.2 QuicTko uorf C ovariSahtieAf dta ptati5o n
1.3 ProblFeomr mulati7o n
1.3.F1u nctLieoanr nifnrgoE mx ample7s
1.3.L2o sFsu nctio8n s
1.3.G3e neralizEartrioor9n
1.3.C4o variSahtief t9
1.3.M5o delfso Fru nctLieoanr nin1g0
1.3.S6p ecificoafMt oidoenl s1 3
1.4 StructoufTr hei Bso ok 14
1.4.P1a rItIL :e arniunngd eCro variSahtief t1 4
1.4.P2a rItI LIe:a rniCnagu siCnogv ariSahtief t1 7
II LERANINGU NDERC OVARIAETS HIFT
2 FunctiAopnp roximat2i1o n
2.1 Importance-Weightinfgo CrTo evcahrniiSaqhtuieAef dsta ptati2o2n
2.1.I1m portance-WeEiRgMh t2e2d
2.1.A2d aptiIvWeE RM 23
2.1.R3e gularIiWzEeRdM 23
2.2 ExamploefIs m portance-WeRiegghrteesdsM ieotnh ods2 5
2.2.S1q uarLeods Lse: ast-SqRueagrreess si2o6n
2.2.A2b soluLtoes Lse:a st-AbsRoelgurtees si3o0n
2.2.H3u beLro ssH:u beRre gressi3o1n
2.2.D4e adzone-LLionseSsau:rp poVretc tRoerg ressi3o3n
2.3 ExamploefIs m portance-WeCilgahstseidf iMceatthioodns3 5
vi Contents
2.3.S1q uarLeods sF:i shDeirs crimAinnaalnyts i3s6
2.3.L2o gisLtoiscLs o:g istic ReCglraessssiif3oin8e r
2.3.H3i ngLeo sSsu: ppoVretc tMoarc hine3 9
2.3.E4x ponenLtoisasBl:o ostin4g0
2.4 NumeriEcxaalm ple4s0
2.4.R1e gressi4o0n
2.4.C2l assific4a1t ion
2.5 Summarayn dD iscussi4o5n
3 ModeSle lecti4o7n
3.1 Importance-WeAikgahitIkenedf ormation Cr4i7t erion
3.2 Importance-WeSiugbhstpeaIdcn ef ormaCtriiotne ri5o0n
3.2.I1n puDte pendenvcsIe.n puItn dependienn ce
GeneralizEartrAionoran l ysi5s1
3.2.A2p proximaCtoerlryeM cotd els5 3
3.2.I3n put-DepeAnndaelnytos fiG se neralizEartrioor5n 4
3.3 Importance-WeCirgohstse-dV alid6a4t ion
3.4 NumeriEcxaalm ple6s6
3.4.R1e gressi6o6n
3.4.C2l assific6a9t ion
3.5 Summarayn dD iscussi7o0n
4 ImportaEnsctei mati7o3n
4.1 KernDeeln siEtsyt imati7o3n
4.2 Kernel Mean Ma7t5ch ing
4.3 LogisRteigcr essi7o6n
4.4 Kullback-LIemipbolretarEn sctei matPrioocne dur7e8
4.4.A1l gorith7m8
4.4.M2o deSle lectbiyCo rno ss-Valid8a1t ion
4.4.B3a sFiusn ction De8s2i gn
4.5 Least-SqIumaproerst aFnictet in8g3
4.5.A1l gorith8m3
4.5.B2a sFiusn ction aDnedsM iogdne Sle lecti8o4n
4.5.R3e gularizPaattTihro anc kin8g5
4.6 UnconstraLienaesdt -Squares IFmiptotritn8ag7n ce
4.6.A1l gorith8m7
4.6.A2n alyCtoimcp utatoifLo ena ve-OneC-rOousts -Valid8a8t ion
4.7 Numerical Exa8m8p les
4.7.S1e ttin9g0
4.7.I2m portance EsbtyiK mLaitEiP9o 0n
4.7.C3o variSahtieAf dta ptatbiyoI nW LSa ndI WCV 92
4.8 ExperimeCnotmapla riso9n4
4.9 Summary 101
5 DireDcetn sity-ERsattiiom atwiiotDnhi mensionRaeldiutcyt io1n0 3
5.1 DensiDtiyf feriennH ceet ero-DistrSiubbustpiaocn1ea0 l3
5.2 CharacteriozfHa ettieorno -DistrSiubbustpiaocn1ea0 l4
Contents vii
5.3 IdentifHyeitnegr o-DistrSiubbustpiaocn1ea0 l6
5.3.B1a sIidce a 106
5.3.F2i shDeirs crimAinnaalnyts i1s0 8
5.3.L3o caFli shDeirs crimAinnaalnyts i1s0 9
5.4 UsinLgF DA foFri ndiHnegt ero-DistrSiubbustpiaocn1ea1 l2
5.5 Density-ERsattiimoa itnit ohneH etero-DistrSiubbustpiaocn1ea1 l3
5.6 NumeriEcxaalm ple1s1 3
5.6.I1l lustErxaatmipvlee 1 13
5.6.P2e rformaCnocmep ariUssoinn Agr tifDiactiSaae lt s 117
5.7 Summary 121
6 RelattiooS na mplSee lectBiioans 1 25
6.1 HeckmanS'asm plSee lectMioodne l 125
6.2 DistribuCthiaonngaaenl d S amplSee lectBiioans 1 29
6.3 TheT wo-StAelpg orith1m3 1
6.4 RelattiooC no variSahtieAf ptp roach1 34
7 ApplicatoifCo onvsa riSahtieAf dta ptati1o3n7
7.1 Brain-CompIuntteerr fa1c3e7
7.1.B1a ckgroun1d3 7
7.1.E2x perimeSnettaulp 1 38
7.1.E3x perimeRnetsaull t1s4 0
7.2 SpeakIedre ntific1a4t2i on
7.2.B1a ckgroun1d4 2
7.2.F2o rmulati1o4n2
7.2.E3x perimeRnetsaull t1s4 4
7.3 NaturLaaln guaPgreo cessi1n4g9
7.3.F1o rmulati1o4n9
7.3.E2x perimeRnetsaull t1s5 1
7.4 PerceiAvgeePd r edicftriooFmna cIem ages1 52
7.4.B1a ckgroun1d5 2
7.4.F2o rmulati1o5n3
7.4.I3n corporCahtairnagc teroifHs utmiacnAs g eP ercepti1o5n3
7.4.E4x perimeRnetsaull t1s5 5
7.5 HumanA ctivRietcyo gniftriooAmnc celeromDeattrai 1c5 7
7.5.B1a ckgroun1d5 7
7.5.I2m portance-Weighted Least-Squares 1P5r7o babilistic Classifier
7.5.E3x perimeRnetsaull t1s6 0
7.6 SamplRee usienR einforceLmeeanrtn in1g6 5
7.6.M1a rkoDve cisPiroonb lem1s6 5
7.6.P2o liIctye rati1o6n6
7.6.V3a luFeu nctAipopnr oximat1i6o7n
7.6.S4a mplRee usbey C ovariSahtieAf dta ptati1o6n8
7.6.O5n -PolviscO.yf f-Poli1c6y9
7.6.I6m portaWnecieg htiinnV ga luFeu nctAipopnr oximat1i7o0n
7.6.A7u tomaSteilce ctoifto hneF lattePnairnagm ete1r7 4
viii Contents
7.6.S8a mplRee usPeo liIctye rati1o7n5
7.6.R9o boCto ntrEoxlp erimen1t7s6
III LERANINGC AUSINCGO VARIAETS HIFT
8 ActiLveea rnin1g8 3
8.1 Prelimina1r8i3e s
8.1.S1e tup1 83
8.1.D2e composiotfGi eonne ralizEartrioor1n 8 5
8.1.B3a sSitcr atoefgA yc tiLveea rnin1g8 8
8.2 PopulationA-cBtaisLveeeda rniMnegt hods1 88
8.2.C1l assMiectahlo odf A ctiLveea rnifnoCrgo rreMcotd els1 89
8.2.L2i mitatoifCo lnass sAipcparlo aacnhdC ountermeasu1r9e0s
8.2.I3n put-IndepVeanrdieanntc e-MOentlhyo d 191
8.2.I4n put-DepeVnadreinatn ce-MOentlhyo d 193
8.2.I5n put-IndepBeinadse-natn d-VAaprpiraonacceh1 95
8.3 NumeriEcxaalm ploefPs o pulationA-cBtaisLveeeda rniMnegt hods1 98
8.3.S1e tup1 98
8.3.A2c curacGye noefr alizEartrEiosortn i mati2o0n0
8.3.O3b tainGeedn eralizErartoiro2 n0 2
8.4 Pool-BaAscetdiL veea rniMnegt hods2 04
8.4.C1l assAicctailLv eea rniMnegt hofdo Cro rreMcotd elasn d
ItLsi mitati2o0n4s
8.4.I2n put-IndepVeanrdieanntc e-MOentlhyo d 205
8.4.I3n put-DepeVnadreinatn ce-MOentlhyo d 206
8.4.I4n put-IndepBeinadse-natn d-VAaprpiraonacceh2 07
8.5 NumeriEcxaalm ploefPs o ol-BaAscetdiL veea rniMnegt hods2 09
8.6 Summarayn dD iscussi2o1n2
9 ActiLveea rniwnigtM ho deSle lecti2o1n5
9.1 DireAcptp roaacnhdt heA ctiLveea rning/MSoedleelc tDiiolne mma2 15
9.2 Sequential Ap2p1r6o ach
9.3 BatcAhp proach2 18
9.4 EnsembAlcet iLveea rnin2g1 9
9.5 NumeriEcxaalm ple2s2 0
9.5.S1e ttin2g2 0
9.5.A2n alyosfiB sa tcAhp proach2 21
9.5.A3n alyosfiS se quenAtpiparlo ach2 22
9.5.C4o mpariosfoO nb tainGeedn eralizEartrioor2n 2 2
9.6 Summarayn dD iscussi2o2n3
10 ApplicatoifAo cntsi Lveea rnin2g25
10.1D esiogfnE fficiEexnptl oraSttiroant eignRi eeisn forceLmeeanrtn in2g2 5
10.1.E1f ficient ExwpiltoAhrc attiiLvoeena rnin2g2 5
10.1.R2e inforceLmeeanrtn iRnegv isit2e2d6
10.1.D3e composiotfGi eonne ralizEartrioor2n 2 8
Contents ix
10.1.E4s timaGteinnegr alizEartrfiooorArn c tiLveea rnin2g2 9
10.1.D5e signSianmgp liPnogl ici2e3s0
10.1.A6c tiLveea rniinnPg o liIctye rati2o3n1
10.1.R7o boCto ntrEoxlp erimen2t3s2
10.2W afeArl ignmiennS te miconduEcxtpoors uArpep aratu2s3 4
IV CONCLUSIONS
11 ConclusiaonndFs u tuPrreo spec2t4s1
11.1C onclusio2n4s1
11.2F utuPrreo spec2t4s2
AppendLiixso:tf S ymboalnsd A bbreviati2o4n3s
Bibliogra2p4h7y
Index2 59
Foreword
Modermna chinlee arnfiancge asn umbeorf g rancdh allenTgheese .v egrr ow­
ingW orldW ideW eb,h ight hroughmpeutth odisng enomicasn,dm odern
imagimnegt hodisnb raisnc ientcoen ,a mej usatf ewp,o seev elra rgperro b­
lemsw herlee arnimnegt hodnse edt os calteo,i ncreatshee ierf ficienacnyd,
algoritnhemesdt ob ecomaeb lteo d eawli thm illion-dimeinnspiuoatntsa l
terabyotfed sa taA.tt hes amet imiet b ecomemso rea ndm orei mporttaon t
efficienatnldry o bustmloyd elh ighlcyo mplepxr obletmhsa atr es tructured
(e.ga. g,r ammaurn derltiheeds a taa)n de xhibniotn linbeeahra viIonra .d di­
tiodna,t far otmh er eawlo rladr et ypicanlolny- stastoit hoenriaesary n ,ee tdo
compensfaottreh en on-statiaosnpaercoytf ts h ed atian o rdetrom apt hep rob­
lemb actko s tationFairniatlywl.hy e,ne xplaintihnawgth ilmea chinlee arning
andm odersnt atisgteincesr aat vea sntu mbeorf a lgoritthhmastt a cktlhee
abovceh allenigteb se,c omeisn creasiinmgployr tfaonrtt hep ractitniootn er
onltyo p rediacntdg eneralwiezleol n u nseedna tbau tt oa lstoo e xplatihne
nonlinperaerd iclteiavren miancgh inteh,ai ts t,o h arvetshtep redictciaopna ­
biliftoyrm akinign ferenacbeosut th ew orltdh awti lclo ntribtuoat bee tter
understanodfti hnesg c iences.
The presebnoto kc ontribtuoto ensea speocftt hea bove-mentigornaendd
challenngaemse:l tyh,ew orlodf n on-statidoantaiarsa y d dressCelda.s sically,
learnailnwga yass sumtehsa tth eu nderlypirnogb abidliisttyr ibouftt ihodena ta
fromw hicihn fereinsmc aed es taytsh es ameI.n o thewro rdsi,ti su nderstood
thatth eriesn oc hangiend istribbuettiwoente hnse amplfer omw hicwhe l earn
toa ndt hen ove(lu nseeonu)t -of-sadmaptlaIe.n m anyp ractisceatlt itnhgiss
assumptiisio nnc orraencdtt h,u sst andaprrde dicwtiilollni kebleys uboptimal.
The presebnoto kv ersyu ccessfauslsleym bltehses tate-of-the-art research
resulotns l earniinngn on-statieonnvairryo nments-aw fiotchu osn the
covarisahtiemf otd el-anhda se mbeddetdh ibso dyo fw orki nttoh eg eneral
literaftruormme a chinlee arni(nsge misuperlveiarsneidno gn,l inlee arning,