Article In: orcid, cienciavitae, scopus
Algorithms for Projection - Pursuit robust principal component analysis
Chemometrics and Intelligent Laboratory Systems
— 2007 — Elsevier
Key information
Authors:
Published in
06/15/2007
Abstract
The results of a standard principal component analysis (PCA) can be affected by the presence of outliers. Hence robust alternatives to {PCA} are needed. One of the most appealing robust methods for principal component analysis uses the ProjectionxPursuit principle. Here, one projects the data on a lower-dimensional space such that a robust measure of variance of the projected data will be maximized. The ProjectionxPursuit-based method for principal component analysis has recently been introduced in the field of chemometrics, where the number of variables is typically large. In this paper, it is shown that the currently available algorithm for robust ProjectionxPursuit {PCA} performs poor in the presence of many variables. A new algorithm is proposed that is more suitable for the analysis of chemical data. Its performance is studied by means of simulation experiments and illustrated on some real data sets.
Publication details
Authors in the community:
Publication version
AO - Author's Original
Publisher
Elsevier
Link to the publisher's version
https://www.sciencedirect.com/science/article/abs/pii/S016974390700007X
Title of the publication container
Chemometrics and Intelligent Laboratory Systems
First page or article number
218
Last page
225
Volume
87
Issue
2
Fields of Science and Technology (FOS)
mathematics - Mathematics
Publication language (ISO code)
eng - English
Alternative identifier (URI)
https://doi.org/10.1016/j.chemolab.2007.01.004
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