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...

基于markov随机游走的谱聚类相似图构造方法

DOI: 10.13232/j.cnki.jnju.2015.04.015, PP. 772-780

Keywords: 谱聚类,马尔可夫随机游走,近邻图,转移概率矩阵

Full-Text   Cite this paper   Add to My Lib

Abstract:

谱聚类是一种基于图谱理论的聚类方法由样本数据构成的相似图是谱聚类的基础,也是影响谱聚类性能的一个重要因素提出一种基于markov随机游走模型的稀疏相似图构造方法提出的方法在常规的k最近邻图上定义一个markov随机游走点,利用游走点的高阶转移概率来选择近邻点由于高阶转移概率反映的是数据间多层复杂的关联程度,因此通过高阶转移概率确定的近邻数据更可靠在人工仿真和实际数据集上的对比实验表明,提出的方法较常规的近邻图能更好地反映存在数据中的结构,提高谱聚类的效果

References

[1]  .huanghc,chuangyy,chencs.affinityaggregationforspectralclustering.in:ieeeconferenceoncomputervisionandpatternrecognition,
[2]  .高尚兵周静波严云洋.一种新的基于超像素的谱聚类图像分割算法.南京大学学报(自然科学),2013,49(2):169175.
[3]  .luxburgu.atutorialonspectralclustering.statisticsandcomputing,2007,17(4):395-416.
[4]  .pavanm,pelillom.dominantsetsandpairwiseclustering.ieeetransactionsonpatternanalysisandmachineintelligence,2007,29(1):167-162.
[5]  .zhangx,youq.animprovedspectralclusteringalgorithmbasedonrandomwalk.frontiersofcomputerscienceinchina,2012,(3):268-278.
[6]  .meilam,shij.arandomwalksviewofspectralsegmentation.in:the8thinternationalworkshoponartificialintelligenceandstatistics,floridaus,2001.
[7]  .donathwe,hoffmanaj.lowerboundsforpartitioningofgraphs.ibmjournalofresearchanddevelopment,1973,17:420-425.
[8]  .nga,jordanm,weissy.onspectralclustering:analysisandanalgorithm.advancesinneuralinformationprocessingsystems,2002:849-856.
[9]  .yangx,lateckilj.affinitylearningonatensorproductgraphwithapplicationstoshapeandimageretrieval.in:ieeeconferenceoncomputervisionandpatternrecognition,2011:2369-2376.
[10]  .fiedlerm.algebraicconnectivityofgraphs.czechoslovakmathematicaljournal,1973,23:298-305.
[11]  .kadimt.vectorquantizationbasedapproximatespectralclusteringoflargedatasets.patternrecognition,2012,45(8):3034-3044.
[12]  .王玲,薄列峰,焦李成.密度敏感的半监督谱聚类.软件学报,2007,18(10):2412-2422.
[13]  .王娜,李霞.基于监督信息特性的主动半监督谱聚类算法.电子学报,2010,38(1):172-176.
[14]  .caoj,chenp,daiq,etal.localinformation-basedfastapproximatespectralclustering.patternrecognitionletters,2014,28:63-69.
[15]  washingtondc,usa:ieeecomputersociety,2011:794一801.
[16]  changh,yeungdy.robustpath-basedspectralclustering.patternrecognition,2008,41(1):191一203.
[17]  zhangx,lij,yuh.localdensityadaptivesimilaritymeasurementforspectralclusteringpatternrecognitionletters,2011,32(2):352一358.
[18]  .jainak,murtym,andflynnp.dataclustering:areview.acmcomputingsurveys,1999,31(3):264-323.
[19]  .luh,fuz,shux.non-negativeandsparsespectralclustering.patternrecognition,2014,47(1):418-426.
[20]  washingtondc,usa:ieeecomputersociety,2012:773-780.
[21]  .纳跃跃,于剑.一种用于谱聚类图像分割的像素相似度计算方法.南京大学学报(自然科学),2013,49(2):199-168.
[22]  .shij,malikj.normalizedcutsandimagesegmentation.ieeetransactionsonpatternanalysisandmachineintelligence,2000,22:888-905.
[23]  .田铮,李小斌,句彦伟.谱聚类的扰动分析.中国科学(e辑:信息科学),2007,37(4):527-543.
[24]  .吴健,崔志明,时玉杰.基于局部密度构造相似矩阵的谱聚类算法,通信学报,2013,34(3):14-22.
[25]  .zhoud,bousqueto,laltn,etal.learningwithlocalandglobalconsistency.advancesinneuralinformationprocessingsystems,mitpress,2004:321-328.
[26]  .themnistdatabaseofhandwrittendigits.http://yann.lecun.com/exdb/mnist/,2015-03-01.
[27]  .汪荣鑫.随机过程.西安:西安交通大学出版社,1987.278.
[28]  tishbyn,slonimn.dataclusteringbymarkovianrelaxationandtheinformationbottleneckmethod.advancesinneural
[29]  informationprocessingsystems.mitpress,2000,640一646.
[30]  szummerm,jaakkolat.partiallylabeledclassificationwithmarkovrandomwalks.advancesinneuralinformationprocessing
[31]  systems,mitpress,2002,945一952.
[32]  jungj,wangb,tuz.unsupervisedmetriclearningbysell-smoothingoperator.in:ieeeconferenceoncomputervision(iccv)

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133