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Testing of Feature Extracting Methods on Bar Problem

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F07%3A00087572" target="_blank" >RIV/67985807:_____/07:00087572 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Testing of Feature Extracting Methods on Bar Problem

  • Original language description

    Compared is performance of several dimension reduction techniques as a tool for feature extraction. Studied are namely singular value decomposition, FastMap, semi-discrete decomposition, non-negative matrix factorization, novel neural network based algorithm for Boolean factor analysis and three frequently used cluster analysis methods as well. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Resulting images show that Boolean factor analysis is upmost suitable method for this kind of data.

  • Czech name

    Testování metod pro extrakci příznaků na problému linií.

  • Czech description

    Porovnávnává se několik metod pro redukci dimenze z hlediska jejich možného využití pro extrakci příznaků. Porovnávany jsou zejména metody SVD, FastMap, SDD, NMF, nový neuronový algoritmus BFA a tři často požívané metody.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GA201%2F05%2F0079" target="_blank" >GA201/05/0079: Formal concept analysis of indeterminate and large data: theory, methods, and applications</a><br>

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2007

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Statistics for Data Mining, Learning and Knowledge Extraction

  • ISBN

    978-90-73592-26-1

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    1-8

  • Publisher name

    University of Aveiro

  • Place of publication

    Aveiro

  • Event location

    Aveiro

  • Event date

    Aug 30, 2007

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article