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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F03%3A03091305%21RIV%2F2004%2FMSM%2F212304%2FN
rdf:type
n16:Vysledek skos:Concept
dcterms:description
Probabilistic subspace mixture models, as proposed over the last few years, are interesting methods for learning image manifolds, i.e. nonlinear subspaces of spaces in which images are represented as vectors by their grey-values. However, for many practical applications, where outliers are common, these methods still lack robustness. Here, the idea of robust mixture modelling by t-distributions is combined with probabilistic subspace mixture models. The resulting robust subspace mixture model is shown experimentally to give advantages in density estimation and classification of image data sets Probabilistic subspace mixture models, as proposed over the last few years, are interesting methods for learning image manifolds, i.e. nonlinear subspaces of spaces in which images are represented as vectors by their grey-values. However, for many practical applications, where outliers are common, these methods still lack robustness. Here, the idea of robust mixture modelling by t-distributions is combined with probabilistic subspace mixture models. The resulting robust subspace mixture model is shown experimentally to give advantages in density estimation and classification of image data sets
dcterms:title
Rubust subspace mixture models using $t$-distributions Rubust subspace mixture models using $t$-distributions
skos:prefLabel
Rubust subspace mixture models using $t$-distributions Rubust subspace mixture models using $t$-distributions
skos:notation
RIV/68407700:21230/03:03091305!RIV/2004/MSM/212304/N
n3:strany
319 ; 328
n3:aktivita
n15:Z
n3:aktivity
Z(MSM 212300013)
n3:dodaniDat
n13:2004
n3:domaciTvurceVysledku
n9:2053586
n3:druhVysledku
n4:D
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
626183
n3:idVysledku
RIV/68407700:21230/03:03091305
n3:jazykVysledku
n17:eng
n3:klicovaSlova
EM;mixture models;t-distribution
n3:klicoveSlovo
n11:mixture%20models n11:EM n11:t-distribution
n3:kontrolniKodProRIV
[537E6264B2CE]
n3:mistoKonaniAkce
Norwich
n3:mistoVydani
London
n3:nazevZdroje
BMVC 2003: Proceedings of the 14th British Machine Vision Conference
n3:obor
n18:JD
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:rokUplatneniVysledku
n13:2003
n3:tvurceVysledku
De Ridder, D. Franc, Vojtěch
n3:typAkce
n5:WRD
n3:zahajeniAkce
2003-09-09+02:00
n3:zamer
n21:MSM%20212300013
s:numberOfPages
10
n20:hasPublisher
British Machine Vision Association
n14:isbn
1-901725-23-5
n6:organizacniJednotka
21230