Handling noise in Boolean matrix factorization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F17%3A73582745" target="_blank" >RIV/61989592:15310/17:73582745 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.24963/ijcai.2017/198" target="_blank" >http://dx.doi.org/10.24963/ijcai.2017/198</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24963/ijcai.2017/198" target="_blank" >10.24963/ijcai.2017/198</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Handling noise in Boolean matrix factorization

Popis výsledku v původním jazyce

We critically examine and point out weaknesses of the existing considerations in Boolean matrix factorization (BMF) regarding noise and the algorithms&apos; ability to deal with noise. We argue that the current understanding is underdeveloped and that the current approaches are missing an important aspect. We provide a new, quantitative way to assess the ability of an algorithm to handle noise. Our approach is based on a common-sense definition of robustness requiring that the computed factorizations should not be affected much by varying the noise in data. We present an experimental evaluation of several existing algorithms and compare the results to the observations available in the literature. In addition to providing justification of some properties claimed in the literature without proper justification, our experiments reveal properties which were not reported as well as properties which counter certain claims made in the literature. Importantly, our approach reveals a line separating robust-to-noise from sensitive-to-noise algorithms, which has not been revealed by the previous approaches.

Název v anglickém jazyce

Handling noise in Boolean matrix factorization

Popis výsledku anglicky

We critically examine and point out weaknesses of the existing considerations in Boolean matrix factorization (BMF) regarding noise and the algorithms&apos; ability to deal with noise. We argue that the current understanding is underdeveloped and that the current approaches are missing an important aspect. We provide a new, quantitative way to assess the ability of an algorithm to handle noise. Our approach is based on a common-sense definition of robustness requiring that the computed factorizations should not be affected much by varying the noise in data. We present an experimental evaluation of several existing algorithms and compare the results to the observations available in the literature. In addition to providing justification of some properties claimed in the literature without proper justification, our experiments reveal properties which were not reported as well as properties which counter certain claims made in the literature. Importantly, our approach reveals a line separating robust-to-noise from sensitive-to-noise algorithms, which has not been revealed by the previous approaches.

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

<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

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

26th International Joint Conference on Artificial Intelligence, IJCAI 2017

ISBN

978-0-9992411-0-3

ISSN

1045-0823

e-ISSN

neuvedeno

Počet stran výsledku

7

Strana od-do

1433-1439

Název nakladatele

International Joint Conferences on Artificial Intelligence

Místo vydání

Melbourne

Místo konání akce

Melbourne

Datum konání akce

19. 8. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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