Boolean matrix factorization with background knowledge
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F22%3A73613673" target="_blank" >RIV/61989592:15310/22:73613673 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S095070512200082X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S095070512200082X</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.knosys.2022.108261" target="_blank" >10.1016/j.knosys.2022.108261</a>
Alternative languages
Result language
angličtina
Original language name
Boolean matrix factorization with background knowledge
Original language description
Boolean matrix factorization (BMF) is a popular data analysis method summarizing the input data by Boolean factors. The Boolean nature ensures an easy interpretation of a particular factor, however, the interpretation of all discovered factors (as a whole) by domain experts may be difficult as the BMF methods seek only information in the data and do not reflect the experts understanding of data. In the paper, we propose a formalization of a novel variant of BMF reflecting expert's background knowledge—additional knowledge about the data—that is not part of the data, in the form of attribute weights, as well as an algorithm for it. Moreover, we show that the proposed algorithm, which significantly outperforms the state-of-the-art algorithm, provides encouraging results that are worth further investigation.
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
2022
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
241
Issue of the periodical within the volume
APR
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
6
Pages from-to
"108261-1"-"108261-6"
UT code for WoS article
000788730900010
EID of the result in the Scopus database
2-s2.0-85124302917