Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 87, Issue 2, Pages 218-225Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2007.01.004
Keywords
multivariate statistics; optimization; numerical precision; outliers; robustness; scale estimators
Ask authors/readers for more resources
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 Projection-Pursuit 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 Projection-Pursuit-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 Projection-Pursuit 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. (c) 2007 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available