Rubust subspace mixture models using $t$-distributions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F03%3A03091305" target="_blank" >RIV/68407700:21230/03:03091305 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Rubust subspace mixture models using $t$-distributions
Original language 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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
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Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2003
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
BMVC 2003: Proceedings of the 14th British Machine Vision Conference
ISBN
1-901725-23-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
319-328
Publisher name
British Machine Vision Association
Place of publication
London
Event location
Norwich
Event date
Sep 9, 2003
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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