Title of article
A comparison between two robust PCA algorithms
Author/Authors
Stanimirova، نويسنده , , I and Walczak، نويسنده , , B and Massart، نويسنده , , D.L and Simeonov، نويسنده , , V، نويسنده ,
Issue Information
دوفصلنامه با شماره پیاپی سال 2004
Pages
13
From page
83
To page
95
Abstract
The article reports the results of a comparative study of two robust Principal Component Analysis (PCA) algorithms based on Projection Pursuit which can be used for exploratory data analysis. The first one is proposed by Croux and Ruiz-Gazen, denoted as C–R algorithm, and the second one by Hubert et al., introducing its modified version, abbreviated as RAPCA. They are applied to uniformly distributed simulated data sets, chemical data sets [environmental and near infrared (NIR) spectra] containing various numbers of variables and objects, as well as different observationsʹ structure. Their performance and features, what they offer, are discussed in detail.
Keywords
Projection pursuit , Robust scale , Robust PCA , Classical scale
Journal title
Chemometrics and Intelligent Laboratory Systems
Serial Year
2004
Journal title
Chemometrics and Intelligent Laboratory Systems
Record number
1460905
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