Boolean Matrix Factorization for Data with Symmetric Variables
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F22%3A73615102" target="_blank" >RIV/61989592:15310/22:73615102 - isvavai.cz</a>
Result on the web
<a href="https://obd.upol.cz/id_publ/333194989" target="_blank" >https://obd.upol.cz/id_publ/333194989</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDM54844.2022.00123" target="_blank" >10.1109/ICDM54844.2022.00123</a>
Alternative languages
Result language
angličtina
Original language name
Boolean Matrix Factorization for Data with Symmetric Variables
Original language description
Boolean matrix factorization (BMF), a popular methodology of preprocessing and analyzing 1/0 tabular data, generally handles 0s and 1s differently. It aims to explain 1s in the data by factors, while 0s are just left unexplained. This difference is mainly given by the usual data character, where 1s carry much more important information (and are much scarcer) than 0s. However, in some datasets, the 1s and 0s are equally important. Such datasets require symmetrical handling of 1s and 0s. We propose a novel factorization of such data and its algorithm. Unlike usual BMF methods, factors are linearly ordered by priority in our factorization, and factors can contradict each other – meaning that one factor can put 1 where the other puts 0. In such a case, the factor with higher priority is right. We show that the proposed factorization provides a more compact data description than a straightforward application of the usual BMF methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE International Conference on Data Mining (ICDM)
ISBN
978-1-66545-099-7
ISSN
1550-4786
e-ISSN
2374-8486
Number of pages
6
Pages from-to
1011-1016
Publisher name
The Institute of Electrical and Electronics Engineers
Place of publication
Piscataway
Event location
Orlando, Florida
Event date
Nov 28, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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