Boolean matrix factorization for symmetric binary variables

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Boolean matrix factorization for symmetric binary variables

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Boolean matrix factorization for symmetric binary variables

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

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

2023

Kód důvěrnosti údajů

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

Název periodika

KNOWLEDGE-BASED SYSTEMS

ISSN

0950-7051

e-ISSN

1872-7409

Svazek periodika

279

Číslo periodika v rámci svazku

NOV

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

"110944-1"-"110944-15"

Kód UT WoS článku

001080411500001

EID výsledku v databázi Scopus

2-s2.0-85170406410