Boolean matrix factorization with background knowledge

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Boolean matrix factorization with background knowledge

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

Boolean matrix factorization with background knowledge

Popis výsledku anglicky

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&apos;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.

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

2022

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

241

Číslo periodika v rámci svazku

APR

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

"108261-1"-"108261-6"

Kód UT WoS článku

000788730900010

EID výsledku v databázi Scopus

2-s2.0-85124302917