Some Regularized Tools for Dimensionality Reduction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F25%3A00642337" target="_blank" >RIV/67985807:_____/25:00642337 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-032-03708-4_20" target="_blank" >https://doi.org/10.1007/978-3-032-03708-4_20</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-032-03708-4_20" target="_blank" >10.1007/978-3-032-03708-4_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Some Regularized Tools for Dimensionality Reduction

Popis výsledku v původním jazyce

Dimensionality reduction has become a commonly used part of the analysis of complex economic data. The aim of this work is to study the potential of regularized tools for dimensionality reduction methods and possibly to propose some novel regularized tools. The regularization in the form of shrinkage allows to improve numerical stability of the tools for high-dimensional data and also to reduce variability of parameter estimates at the cost of introducing bias. Firstly, a robust regularized version of the coefficient of multiple correlation is proposed, which may be exploited within a Minimum Relevance Maximum Redundancy supervised variable selection. Secondly, the ridge regularization is discussed not to bring any modification of principal component analysis, this is true also for robust versions of principal component analysis.

Název v anglickém jazyce

Some Regularized Tools for Dimensionality Reduction

Popis výsledku anglicky

Dimensionality reduction has become a commonly used part of the analysis of complex economic data. The aim of this work is to study the potential of regularized tools for dimensionality reduction methods and possibly to propose some novel regularized tools. The regularization in the form of shrinkage allows to improve numerical stability of the tools for high-dimensional data and also to reduce variability of parameter estimates at the cost of introducing bias. Firstly, a robust regularized version of the coefficient of multiple correlation is proposed, which may be exploited within a Minimum Relevance Maximum Redundancy supervised variable selection. Secondly, the ridge regularization is discussed not to bring any modification of principal component analysis, this is true also for robust versions of principal component analysis.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA25-15490S" target="_blank" >GA25-15490S: LEDNeCo: Nízkoenergetické hluboké neurovýpočty</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Intelligence and Soft Computing. 24th International Conference ICAISC 2025, Proceedings Part II

ISBN

978-3-032-03707-7

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

11

Strana od-do

243-253

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Zakopane

Datum konání akce

22. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—