How to Reduce Dimensionality of Data: Robustness Point of View

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00444728" target="_blank" >RIV/67985807:_____/15:00444728 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.5937/sjm10-6531" target="_blank" >http://dx.doi.org/10.5937/sjm10-6531</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5937/sjm10-6531" target="_blank" >10.5937/sjm10-6531</a>

Jazyk výsledku

angličtina

Název v původním jazyce

How to Reduce Dimensionality of Data: Robustness Point of View

Popis výsledku v původním jazyce

Data analysis in management applications often requires to handle data with a large number of variables. Therefore, dimensionality reduction represents a common and important step in the analysis of multivariate data by methods of both statistics and data mining. This paper gives an overview of robust dimensionality procedures, which are resistant against the presence of outlying measurements. A simulation study represents the main contribution of the paper. It compares various standard and robust dimensionality procedures in combination with standard and robust methods of classification analysis. While standard methods turn out not to perform too badly on data which are only slightly contaminated by outliers, we give practical recommendations concerning the choice of a suitable robust dimensionality reduction method for highly contaminated data. Namely the highly robust principal component analysis based on the projection pursuit approach turns out to yield the most satisfactory resul

Název v anglickém jazyce

How to Reduce Dimensionality of Data: Robustness Point of View

Popis výsledku anglicky

Data analysis in management applications often requires to handle data with a large number of variables. Therefore, dimensionality reduction represents a common and important step in the analysis of multivariate data by methods of both statistics and data mining. This paper gives an overview of robust dimensionality procedures, which are resistant against the presence of outlying measurements. A simulation study represents the main contribution of the paper. It compares various standard and robust dimensionality procedures in combination with standard and robust methods of classification analysis. While standard methods turn out not to perform too badly on data which are only slightly contaminated by outliers, we give practical recommendations concerning the choice of a suitable robust dimensionality reduction method for highly contaminated data. Namely the highly robust principal component analysis based on the projection pursuit approach turns out to yield the most satisfactory resul

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-17187S" target="_blank" >GA13-17187S: Konstrukce pokročilých srozumitelných klasifikátorů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Serbian Journal of Management

ISSN

1452-4864

e-ISSN

—

Svazek periodika

10

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

RS - Srbská republika

Počet stran výsledku

10

Strana od-do

131-140

Kód UT WoS článku

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EID výsledku v databázi Scopus

2-s2.0-84927920065