On Coupling Robust Estimation with Regularization for High-Dimensional Data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F17%3A00474072" target="_blank" >RIV/67985807:_____/17:00474072 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-55723-6_2" target="_blank" >http://dx.doi.org/10.1007/978-3-319-55723-6_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-55723-6_2" target="_blank" >10.1007/978-3-319-55723-6_2</a>

Result language

angličtina

Original language name

On Coupling Robust Estimation with Regularization for High-Dimensional Data

Original language description

Standard data mining procedures are sensitive to the presence of outlying measurements in the data. Therefore, robust data mining procedures are highly desirable, which are resistant to outliers. This work has the aim to propose new robust classification procedures for high-dimensional data and algorithms for their efficient computation. Particularly, we use the idea of implicit weights assigned to individual observation to propose several robust regularized versions of linear discriminant analysis (LDA), suitable for data with the number of variables exceeding the number of observations. The approach is based on a regularized version of the minimum weighted covariance determinant (MWCD) estimator and represents a unique attempt to combine regularization and high robustness, allowing to down-weight outlying observations. Classification performance of new methods is illustrated on real fMRI data acquired in neuroscience research.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

<a href="/en/project/GA13-23940S" target="_blank" >GA13-23940S: Personality and spontaneous brain activity during rest and movie watching: relation and structural determinants</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

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

Article name in the collection

Data Science. Innovative Developments in Data Analysis and Clustering

ISBN

978-3-319-55722-9

ISSN

1431-8814

e-ISSN

—

Number of pages

13

Pages from-to

15-27

Publisher name

Springer

Place of publication

Cham

Event location

Bologna

Event date

Jul 5, 2015

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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