Parallel exploration of partial solutions in Boolean matrix factorization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F19%3A73595308" target="_blank" >RIV/61989592:15310/19:73595308 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0743731518306968" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0743731518306968</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jpdc.2018.09.014" target="_blank" >10.1016/j.jpdc.2018.09.014</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parallel exploration of partial solutions in Boolean matrix factorization

Popis výsledku v původním jazyce

Boolean matrix factorization (BMF) is a well established method for preprocessing and analysis of data. There is a number of algorithms for BMF, but none of them uses benefits of parallelization. This is mainly due to the fact that many of the algorithms utilize greedy heuristics that are inherently sequential. In this work, we propose a general parallelization scheme for BMF in which several locally optimal partial matrix decompositions are constructed simultaneously in parallel, instead of just one in a sequential algorithm. As a result of the computation, either the single best final decomposition or several top-k of them may be returned. The scheme can be applied to any sequential heuristic BMF algorithm and we show the application on two representative algorithms, namely GRECOND and ASSO. Improvements in decompositions are presented via results from experiments with the new algorithms on synthetic and real datasets

Název v anglickém jazyce

Parallel exploration of partial solutions in Boolean matrix factorization

Popis výsledku anglicky

Boolean matrix factorization (BMF) is a well established method for preprocessing and analysis of data. There is a number of algorithms for BMF, but none of them uses benefits of parallelization. This is mainly due to the fact that many of the algorithms utilize greedy heuristics that are inherently sequential. In this work, we propose a general parallelization scheme for BMF in which several locally optimal partial matrix decompositions are constructed simultaneously in parallel, instead of just one in a sequential algorithm. As a result of the computation, either the single best final decomposition or several top-k of them may be returned. The scheme can be applied to any sequential heuristic BMF algorithm and we show the application on two representative algorithms, namely GRECOND and ASSO. Improvements in decompositions are presented via results from experiments with the new algorithms on synthetic and real datasets

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

<a href="/cs/project/GA15-17899S" target="_blank" >GA15-17899S: Rozklady matic s booleovskými a ordinálními daty: teorie a algoritmy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING

ISSN

0743-7315

e-ISSN

—

Svazek periodika

123

Číslo periodika v rámci svazku

JAN

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

180-191

Kód UT WoS článku

000451108900016

EID výsledku v databázi Scopus

2-s2.0-85054908648