Handling noise in Boolean matrix factorization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F18%3A73588398" target="_blank" >RIV/61989592:15310/18:73588398 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0888613X17305029" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0888613X17305029</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijar.2018.03.006" target="_blank" >10.1016/j.ijar.2018.03.006</a>
Alternative languages
Result language
angličtina
Original language name
Handling noise in Boolean matrix factorization
Original language description
One of the challenges presented by Boolean matrix factorization consists in what became known as the ability to deal with noise in data. In this paper, we critically examine existing considerations regarding noise, reported results regarding various algorithms' ability to deal with noise, and approaches used to evaluate this ability. We argue that the current understanding is underdeveloped in several respects and, in particular, that the present way to assess the ability to handle noise in data is deficient. We provide a new, quantitative way to assess this ability. Our method is based on a common-sense definition of robustness requiring that the factorizations computed from data should not be affected much by varying the noise in the data. We present an experimental evaluation of several algorithms, and compare the results to the observations available in the literature. The experiments reveal important properties of these algorithms as regards handling noise. In addition to providing methodological justification of some properties claimed in the literature without proper justification, they 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. We conclude by outlining open problems to which the present considerations and experiments lead.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA15-17899S" target="_blank" >GA15-17899S: Decompositions of Matrices with Boolean and Ordinal Data: Theory and Algorithms</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
ISSN
0888-613X
e-ISSN
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Volume of the periodical
96
Issue of the periodical within the volume
MAY
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
78-94
UT code for WoS article
000430902700007
EID of the result in the Scopus database
2-s2.0-85044148679