Simultaneous Visualization of Samples, Features and Multi-Labels
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00467543" target="_blank" >RIV/67985556:_____/16:00467543 - isvavai.cz</a>
Alternative codes found
RIV/61384399:31160/16:00049971
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Simultaneous Visualization of Samples, Features and Multi-Labels
Original language description
Visualization helps us to understand single-label and multi-label classification problems. In this paper, we show several standard techniques for simultaneous visualization of samples, features and multi-classes on the basis of linear regression and matrix factorization. The experiment with two real-life multilabel datasets showed that such techniques are effective to know how labels are correlated to each other and how features are related to labels in a given multi-label classification problem.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BD - Information theory
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 23rd International Conference on Pattern Recognition (ICPR)
ISBN
978-1-5090-4846-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
3592-3597
Publisher name
IEEE
Place of publication
Piscataway
Event location
Cancún
Event date
Dec 4, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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