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1、稀疏判别分析摘要:针对流形嵌入降维方法小在高维空间构建近邻图无益于后续工作,以及不容易给近邻大小和热核参数赋合适值的问题,捉出一种稀疏判别分析算法(seda)o首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构,以克服流形嵌入方法的不足;其次,将稀疏保持作为正则化项使用fisher判别准则,能够得到最优的投影。在一组高维数据集上的实验结果表明,seda是非常有效的半监督降维方法。关键词:判别分析;稀疏表示;近邻图;稀疏图sparsediscriminantanalysischonxiao.dong1linhu
2、an.xiang21.schoolofinformationandengineering,zhejiangradioandtelevisionuniversity,hangzhouzhejiang310030,china■2.schoolofinformationandelectronicengineering,zhejianguniversityofscienceandtechnology,hangzhouzhejiang310023,chinaabstract:methodsformanifoldembedd
3、ingexistsinthefollowingissues:ononehand,neighborhoodgraphisconstructedinsuchthehigh-dimensionalityoforiginalspacethatittendstoworkpoorly;ontheotherhand,appropriatevaluesfortheneighborhoodsizeandheatkernelparameterinvolvedingraphconstructionisgenerallydifficul
4、ttobeassigned・toaddresstheseproblems,anovelsemi-superviseddimensionalityreductionalgorithmcalledsparsecliscriminantanalysis(seda)isproposed・firstly,seclasetsupasparsegraphtopreservetheglobalinformationandgeometricstructureofthedatabasedonsparserepresentation.
5、secondly,itappliesbothsparsegraphandfishercriteriontoseektheoptimalprojection・experimentsonabroadrangeofdatasetsshowthatsedaissuperiortomanypopulardimensionalityreductionmethods・methodsformanifoldembeddinghavethefollowingissues:ononehand,neighborhooclgraphisc
6、onstructedinsuchhigh.dimensionalityoforiginalspacethatittendstoworkpoorly:ontheotherhand,appropriatevaluesfortheneighborhoodsizeandheatkcrnclparameterinvolvedingraphconstructionaregenerallydifficulttobeassigned・toaddresstheseproblems,anewsemi.superviseddimens
7、ionalityreductionalgorithmcalledsparsediscriminantanalysis(seda)wasproposed.firstly,sedasetupasparsegraphtopreservetheglobalinformationandgeometricstructureofthedatabasedonsparserepresentation.secondly,itappliedbothsparsegraphandfishercriteriontoseektheoptima
8、lprojection.theexperimentalresultsonabroadrangeofdatasetsshowthatsedaissuperiortomanypopulardimensionalityreductionmethods・keywords:cliscriminantanalysis;sparserepresentation;neighborhood