Title: Robust sparse principal component analysis
Authors: Croux, Christophe ×
Filzmoser, P.
Fritz, H. #
Issue Date: 2013
Publisher: American Society for Quality Control
Series Title: Technometrics vol:55 issue:2 pages:202-214
Abstract: A method for principal component analysis is proposed that is sparse and robust at the same time. The sparsity delivers principal components that have loadings on a small number of variables, making them easier to interpret. The robustness makes the analysis resistant to outlying observations. The principal components correspond to directions that maximize a robust measure of the variance, with an additional penalty term to take sparseness into account. We propose an algorithm to compute the sparse and robust principal components. The algorithm computes the components sequentially, and thus it can handle datasets with more variables than observations. The method is applied on several real data examples, and diagnostic plots for detecting outliers and for selecting the degree of sparsity are provided. A simulation experiment studies the effect on statistical efficiency by requiring both robustness and sparsity. Supplementary materials are available online on the journal web site.
ISSN: 0040-1706
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Operations Research and Business Statistics (ORSTAT), Leuven
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
RobustSparsePrincipal.pdf Published 541KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members


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

© Web of science