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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&apos; 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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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