Title: Robust PCA and classification in biosciences
Authors: Hubert, Mia ×
Engelen, Sanne #
Issue Date: 2004
Publisher: Oxford univ press
Series Title: Bioinformatics vol:20 issue:11 pages:1728-1736
Abstract: Motivation: Principal components analysis (PCA) is a very popular dimension reduction technique that is widely used as a first step in the analysis of high-dimensional microarray data. However, the classical approach that is based on the mean and the sample covariance matrix of the data is very sensitive to outliers. Also, classification methods based on this covariance matrix do not give good results in the presence of outlying measurements.
ISSN: 1367-4803
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Statistics Section
× corresponding author
# (joint) last author

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