文件名称:paa-master
下载
别用迅雷、360浏览器下载。
如迅雷强制弹出，可右键点击选“另存为”。
失败请重下，重下不扣分。
如迅雷强制弹出，可右键点击选“另存为”。
失败请重下，重下不扣分。
介绍说明－－下载内容均来自于网络，请自行研究使用
概率原型分析软件，语言matlab，属于数据分析软件，非监督学习方法，类似于PCA,NMF等-Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes.
The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations.
In this paper we revisit archetypal analysis the basic principles, and propose a probabilistic fr a mework that accommodates other observation types such as integers, binary, and probability vectors. We
corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data,
and document archetypes based on term-frequency data. We also present an appropriate visualization tool
to summarize archetypal analysis solution better.
The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations.
In this paper we revisit archetypal analysis the basic principles, and propose a probabilistic fr a mework that accommodates other observation types such as integers, binary, and probability vectors. We
corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data,
and document archetypes based on term-frequency data. We also present an appropriate visualization tool
to summarize archetypal analysis solution better.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
paa-master
..........\README.md
..........\examples
..........\........\Disaster-report-aa.R
..........\........\Disaster-report-aa.Rmd
..........\........\Disaster-report-aa.html
..........\........\Disaster-report.R
..........\........\Disaster-report.Rmd
..........\........\Disaster-report.html
..........\........\GSAW97-report-aa.R
..........\........\GSAW97-report-aa.Rmd
..........\........\GSAW97-report-aa.html
..........\........\GSAW97-report.R
..........\........\GSAW97-report.Rmd
..........\........\GSAW97-report.html
..........\........\NIPS-report.R
..........\........\NIPS-report.Rmd
..........\........\NIPS-report.html
..........\........\Soccer-report.R
..........\........\Soccer-report.Rmd
..........\........\Soccer-report.html
..........\........\compareProbVsClassic_Bernoulli.html
..........\........\compareProbVsClassic_Bernoulli.png
..........\........\compareProbVsClassic_Bernoulli_01.png
..........\........\compareProbVsClassic_Poisson.html
..........\........\compareProbVsClassic_Poisson.png
..........\........\compareProbVsClassic_Poisson_01.png
..........\........\compareProbVsClassic_multinomial.html
..........\........\compareProbVsClassic_multinomial.png
..........\........\compareProbVsClassic_multinomial_01.png
..........\........\compareProbVsDefault.html
..........\........\compareProbVsDefault.png
..........\........\compareProbVsDefault_01.png
..........\........\compareProbVsDefault_02.png
..........\........\data_Convergence
..........\........\................\disaster.mat
..........\........\................\guest survey.mat
..........\........\................\nips.mat
..........\........\................\soccer.mat
..........\........\data_Disaster
..........\........\.............\Disaster_aa.Rds
..........\........\.............\Disaster_paa.mat
..........\........\.............\emdata.csv
..........\........\data_GSAW97
..........\........\...........\GSAW97_aa.Rds
..........\........\...........\GSAW97_bin.csv
..........\........\...........\GSAW97_paa.mat
..........\........\data_NIPS
..........\........\.........\NIPS_paa.mat
..........\........\.........\docword.nips.txt
..........\........\.........\useless-words.mat
..........\........\.........\vocab.nips.txt
..........\........\data_Simulation
..........\........\...............\compareProbVsDefault_Bernoulli.mat
..........\........\...............\compareProbVsDefault_Poisson.mat
..........\........\...............\compareProbVsDefault_stochastic.mat
..........\........\...............\uniqMatchesBernoulli.mat
..........\........\...............\uniqMatchesPoisson.mat
..........\........\data_Soccer
..........\........\...........\.DS_Store
..........\........\...........\Soccer_paa.mat
..........\........\demo.html
..........\........\demo.png
..........\........\demo_01.png
..........\........\demo_02.png
..........\........\demo_03.png
..........\........\demo_04.png
..........\........\helpers.R
..........\........\mapCountryData.R
..........\........\plotConvergence.html
..........\........\plotConvergence.m
..........\........\plotConvergence.png
..........\........\plotConvergence_01.png
..........\........\plotProbVsDefault.html
..........\........\plotProbVsDefault.m
..........\........\plotProbVsDefault.png
..........\........\plotProbVsDefault_01.png
..........\scripts
..........\.......\classic_aa.R
..........\.......\classic_aa.m
..........\.......\classic_aa_plot.R
..........\.......\classic_aa_plot.m
..........\.......\classic_aa_test.R
..........\.......\classic_aa_test.m
..........\.......\compareProbVsClassic_Bernoulli.m
..........\.......\compareProbVsClassic_Poisson.m
..........\.......\compareProbVsClassic_multinomial.m
..........\.......\convexCircle.m
..........\.......\demo.m
..........\.......\generate_options.m
..........\.......\paa_Bernoulli.m
..........\.......\paa_Poisson.m
..........\.......\paa_normal.m
..........\.......\paa_stochastic.m