From-below Boolean matrix factorization algorithm based on MDL

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73607829" target="_blank" >RIV/61989592:15310/21:73607829 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s11634-019-00383-6" target="_blank" >https://link.springer.com/article/10.1007/s11634-019-00383-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11634-019-00383-6" target="_blank" >10.1007/s11634-019-00383-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

From-below Boolean matrix factorization algorithm based on MDL

Popis výsledku v původním jazyce

During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, a BMF algorithm performing good results w.r.t. standard measures in BMF is missing. In this paper, we propose a novel from-below Boolean matrix factorization algorithm based on formal concept analysis. The algorithm utilizes the MDL principle as a criterion for the factor selection. On various experiments we show that the proposed algorithm outperforms—from different standpoints—existing state-of-the-art BMF algorithms.

Název v anglickém jazyce

From-below Boolean matrix factorization algorithm based on MDL

Popis výsledku anglicky

During the past few years Boolean matrix factorization (BMF) has become an important direction in data analysis. The minimum description length principle (MDL) was successfully adapted in BMF for the model order selection. Nevertheless, a BMF algorithm performing good results w.r.t. standard measures in BMF is missing. In this paper, we propose a novel from-below Boolean matrix factorization algorithm based on formal concept analysis. The algorithm utilizes the MDL principle as a criterion for the factor selection. On various experiments we show that the proposed algorithm outperforms—from different standpoints—existing state-of-the-art BMF algorithms.

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

2021

Kód důvěrnosti údajů

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

Název periodika

Advances in Data Analysis and Classification

ISSN

1862-5347

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

20

Strana od-do

37-56

Kód UT WoS článku

000574111900001

EID výsledku v databázi Scopus

2-s2.0-85077586121