Boolean Matrix Factorization for Data with Symmetric Variables

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Boolean Matrix Factorization for Data with Symmetric Variables

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Boolean Matrix Factorization for Data with Symmetric Variables

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2022 IEEE International Conference on Data Mining (ICDM)

ISBN

978-1-66545-099-7

ISSN

1550-4786

e-ISSN

2374-8486

Počet stran výsledku

6

Strana od-do

1011-1016

Název nakladatele

The Institute of Electrical and Electronics Engineers

Místo vydání

Piscataway

Místo konání akce

Orlando, Florida

Datum konání akce

28. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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