全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

?马氏度量学习中的几个关键问题研究及几何解释*

DOI: 10.13232/j.cnki.jnju.2013.02.001, PP. 133-141

Keywords: 欧氏距离,马氏距离,度量学习,相似性

Full-Text   Cite this paper   Add to My Lib

Abstract:

?采用距离度量模式的相似性(或不相似性)己广泛应用于模式识别和机器学习等领域.最常用的度量是欧氏距离和马氏距离(mahalanobisdistance).欧氏距离虽然计算相对简单,但由于存在无法结合先验知识、同等看待样木等局限性,常无法满足实际需要.解决此类问题的有效手段之一就是采用非欧氏度量,如马氏度量.马氏度量不仅能够结合数据的统计特性,还能兼顾样木间的相关性.讨论马氏距离度量的相关性质,并给予证明,主要包括:(1)两种度量的区别与联系;(2)在马氏距离度量下导出的点到平面(超平面)距离公式及投影公式;(3)两种度量是距离保持的.最后,给出相关实验验证.

References

[1]  xingep,ngay,jordanmi,etal.distancemetriclearningwithapplicationtoclusteringwithside-in-
[2]  weinbergerkq,saullk.distancemetriclearningforlargemarginnearestneighborclassifica
[3]  guomm,doujh,yangb.programrestructu-ringbasedonconceptsimilaritymeasureandfor-
[4]  铭铭,窦建华,杨彬.基于形式化概念分析和概念相似性度量的程序重组方法.南京大学学报(自然科学),2011,47(5);594-604).
[5]  chopras,hadsellr,lecuny.learningasimiliartymetricdiscriminatively,withapplicationtofaceveri-
[6]  fication.proceedingsoftheieeeconferenceoncomputervisionandpatternrecognition,sandie-go,ca,2005,539一546.
[7]  domeniconic,gunopulosd,pengj.largemarginnearestneighborclassifiers,ieeetransactionson
[8]  goldbergerj,roweiss,hintong,etal.neighbour-hoodcomponentsanalysis.saullk,weissy,bot-
[9]  toul.advancesinneuralinformationprocessingsystems,cambridge;mltpress,ma,2005,17:513~520.
[10]  21thinternationalconferenceonmachinelearning,banff,canada,2004,260一272.
[11]  dons.machinelearning,2007,68:171一200.
[12]  lanckrietgrg,ghaouii.e,bhattacharyyac,etal.arobustminimaxapproachtoclassification.
[13]  journalofmachinelearningresearch,2002,3:555一582.
[14]  mahalanobispc.onthegeneralizeddistanceinstatistics.proceedingsofthenationalinstituteof
[15]  bhattacharyyac,gratelr,jordanmi,etal.robustsparsehyperplaneclassifiers;application
[16]  touncertainmolecularprofilingdata.journalofcomputationalbiology,2004,11(6):1073一1089.doi:10.1089/cmb.2004.11.1073.
[17]  huapress,2005,84-86.(周培德.计算几何:算法分析与设计.第二版.北京:清华大学出版社,2005,84一86).
[18]  yangxb,chensc,yangym.localizedproxi-malsupportvectormachineviageneralizedeigen-
[19]  values.journalofcomputer,2007,30(8):1227一1234.(杨绪兵,陈松灿,杨益民.局部化的]’一义特
[20]  征值最接近支持向量机.计算机学报,2007,30(8):1227一1234).
[21]  formation.advancesinneuralinformationprocessingsystems,2002,521一528.
[22]  tion.journalofmachinelearningresearch2009,10:207一244.
[23]  malconceptanalysis.journalofnanjinguniver-sity(naturalsciences),2011,47(5):594一604(郭
[24]  covert,hartp.nearestneighborpatternclassi-fication.ieeetransactionsoninlormationtheory,1967,it-13:21一27.
[25]  neuralnetworks,2005,16(4):899一909.
[26]  huangk,yangh,kingl,etal.learninglargemat-ginclassifierslocallyandglobally.proceedingsofthe
[27]  yeungds,wangd,ngwwy,etal.structuredlargemarginmachines;sensitivetodatadistribtr
[28]  sciencesofindia,l936,2(1):49一55.
[29]  daih.theoryofmatrices.bcijing;sciencepress,2001,109-177.戴华.矩阵论.北京:科学出版社,2001,109一177).
[30]  bianzq,zhangxg.patternrecognition.beijing;tsinghuapress,2005,117.
[31]  (边肇棋,张学z.模式识别.北京:清华大学出版社,2005,117).
[32]  friedmanjh.regularizeddiscriminantanalysis.journalofamericanstatisticalassociation1989,8:165~175.
[33]  hoffbeckjp,landgrebeda.covaricancematrixestimationandclassificationwithlimitedtrainingda-
[34]  ta.ieeetransactionsonpatternanalysisandma-chinelearning,1996,18(7):763一767.
[35]  zhoupd.coputationgeometry;algorithmanal-ysisanddesign.the2ndedition.beijing;tsing-

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133