Robust Knowledge Discovery from High-Dimensional Data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F12%3A00389647" target="_blank" >RIV/67985807:_____/12:00389647 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Robust Knowledge Discovery from High-Dimensional Data
Original language description
The paper is devoted to advanced robust methods for information extraction from highdimensional data. The concept of knowledge discovery is discussed together with its two important aspects: high dimensionality of the data and sensitivity to the presenceof outlying data values. We propose new robust methods for knowledge discovery suitable for highdimensional data. They are based on the idea of implicit weighting, which is inspired by the least weighted squares regression estimator. We propose a highlyrobust method for a dimension reduction, which can be described as a robust alternative of the principal component analysis based on implicit down-weighting of less reliable data values. Further, we propose a novel robust approach to cluster analysis, which is a popular knowledge discovery method of unsupervised learning. A two-stage cluster analysis method tailor-made for highdimensional data is obtained by combining the robust principal component analysis with the robust cluster analy
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
International collection of scientific work on the occasion of 60th anniversary of university education at faculty of Business Economy with seat in Košice of University of Economics in Bratislava
ISBN
978-80-86175-80-5
ISSN
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e-ISSN
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Number of pages
10
Pages from-to
1-10
Publisher name
Melandrium
Place of publication
Slaný
Event location
Prague
Event date
Sep 13, 2012
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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