Topic detection via ICE

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F19%3A00335960" target="_blank" >RIV/68407700:21340/19:00335960 - isvavai.cz</a>
Result on the web
<a href="http://gams.fjfi.cvut.cz/spms2019" target="_blank" >http://gams.fjfi.cvut.cz/spms2019</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Topic detection via ICE
Original language description
Independent Component Extraction (ICE) is a novel approach based on fundamentals of Independent Component Analysis that deals with the blind source separation. The model assumes a linear mixutre of indepent sources of interest (SOIs) and aims to restore them from the mixture. In ICE, only one SOI is extracted and the rest is considered as a background. Most of the applications of ICA/ICE are in the accusting signal processing. In this paper, the ICE model is used for a blind detection of a single topic in text documents. The results of known unsupervised methods are used for the comparison. The transfer learning is then used to compare ICE also with supervised methods. Pros and cons of all aproaches are discussed.
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of SPMS 2019 - Stochastic and Physical Monitoring Systems
ISBN
978-80-01-06659-1
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
49-55
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Dobřichovice
Event date
Jun 20, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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