Boolean matrix factorization for symmetric binary variables
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F23%3A73620751" target="_blank" >RIV/61989592:15310/23:73620751 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0950705123006949" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0950705123006949</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.knosys.2023.110944" target="_blank" >10.1016/j.knosys.2023.110944</a>
Alternative languages
Result language
angličtina
Original language name
Boolean matrix factorization for symmetric binary variables
Original language description
Binary variables classify into two types: asymmetric variables, where one state (1 or 0) is significantly more valuable than the other, and symmetric variables, where both states are equally valuable. Boolean matrix factorization (BMF), a popular methodology of preprocessing and analyzing tabular binary data, handles its input as asymmetric variables. In the paper, we develop an alternative that handles Boolean matrices as symmetric variables. Our method differs from traditional BMF in that the factors are linearly ordered by priority, and factors can contradict each other, meaning that one factor can assign a value of 1 while the other assigns a value of 0. In such a case, the factor with higher priority is the relevant one. Through experiments, we demonstrate that our approach provides a more compact data description than a straightforward application of the traditional BMF methods. Moreover, it is even able to overcome the Schein rank.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
KNOWLEDGE-BASED SYSTEMS
ISSN
0950-7051
e-ISSN
1872-7409
Volume of the periodical
279
Issue of the periodical within the volume
NOV
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
15
Pages from-to
"110944-1"-"110944-15"
UT code for WoS article
001080411500001
EID of the result in the Scopus database
2-s2.0-85170406410