Title: A fast method for robust principal components with applications to chemometrics
Authors: Hubert, Mia ×
Rousseeuw, Peter
Verboven, S #
Issue Date: 2002
Publisher: Elsevier science bv
Series Title: Chemometrics and intelligent laboratory systems vol:60 issue:1-2 pages:101-111
Abstract: When faced with high-dimensional data, one often uses principal component analysis (PCA) for dimension reduction. Classical PCA constructs a set of uncorrelated variables, which correspond to eigenvectors of the sample covariance matrix. However, it is well-known that this covariance matrix is strongly affected by anomalous observations. It is therefore necessary to apply robust methods that are resistant to possible outliers.
ISSN: 0169-7439
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science