A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00444727" target="_blank" >RIV/67985807:_____/15:00444727 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21460/15:00231271

Výsledek na webu

<a href="http://dx.doi.org/10.1155/2015/320385" target="_blank" >http://dx.doi.org/10.1155/2015/320385</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1155/2015/320385" target="_blank" >10.1155/2015/320385</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Popis výsledku v původním jazyce

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust c

Název v anglickém jazyce

A Robust Supervised Variable Selection for Noisy High-Dimensional Data

Popis výsledku anglicky

The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust c

Druh

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

CEP obor

BA - Obecná matematika

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

BioMed Research International

ISSN

2314-6133

e-ISSN

—

Svazek periodika

2015

Číslo periodika v rámci svazku

Article 320385

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

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Kód UT WoS článku

000356261700001

EID výsledku v databázi Scopus

2-s2.0-84934967829