• Support
  • Contact Us
  • Corporate website
  • Customer Care
  • Training

  • ScienceWatch Home
  • Inside This Month...
  • Interviews

Featured Interviews
Author Commentaries
Institutional Interviews
Journal Interviews
Podcasts

  • Analyses

Featured Analyses
What's Hot In...
Special Topics

  • Data & Rankings

Sci-Bytes
Fast Breaking Papers
New Hot Papers
Emerging Research Fronts
Fast Moving Fronts
Corporate Research Fronts
Research Front Maps
Current Classics
Top Topics
Rising Stars
New Entrants
Country Profiles

  • About Science Watch

Methodology
Archives
Contact Us
RSS Feeds

 ScienceWatch

2009 : January 2009 - Fast Moving Fronts : Mia Hubert, Peter Rousseeuw, & Karlien Vanden Branden

FAST MOVING FRONTS - 2009

January 2009 Download this article
 
Mia Hubert, Peter Rousseeuw, & Karlien Vanden Branden talk with ScienceWatch.com and answer a few questions about this month's Fast Moving Front in the field of Mathematics.
Hubert Article: ROBPCA: A new approach to robust principal component analysis
Authors: Hubert, M;Rousseeuw, PJ;Vanden Branden, K
Journal: TECHNOMETRICS, 47 (1): 64-79, FEB 2005
Addresses: Katholieke Univ Leuven, Dept Math, B-3001 Louvain, Belgium.
Katholieke Univ Leuven, Dept Math, B-3001 Louvain, Belgium.
Univ Antwerp, Dept Math & Comp Sci, B-2020 Antwerp, Belgium.
Katholieke Univ Leuven, Dept Math, B-3001 Heverlee, Belgium.

 Why do you think your paper is highly cited?

Our paper offers a solution to an important problem in statistics and data analysis: how to perform data reduction when the observations may be contaminated with outlying values. This problem is especially important for the analysis of high-dimensional data sets, such as spectral data in chemometrics and genetic data in bio-informatics. We propose an algorithm which is highly robust and computationally feasible, and we also provide a graphical tool for outlier detection.


 
Coautor
 Peter Rousseeuw


 
Coauthor
Karlien Vanden Branden

Moreover, our method serves as the cornerstone of new highly robust calibration methods (principal component regression and partial least squares regression), a robust classifier, and robust multi-way techniques. The availability of user-friendly software in our Matlab toolbox LIBRA has facilitated the practical use of our algorithm. So far applications of our method have been developed in chemometrics, bio-informatics, image analysis, face recognition methods, computer vision, sensory analysis, statistical quality control, and fault detection.

 Does it describe a new discovery, methodology, or synthesis of knowledge?

The new method combines elements of two existing approaches. The first is the minimum covariance determinant method, which dates back to 1984 and for which a fast algorithm was constructed in 1999 (Rousseeuw P and Van Driessen K, "A fast algorithm for the minimum covariance determinant estimator," Technometrics 42: 212-23, 1991). This approach is quite accurate, but is not applicable when there are more dimensions than observations (as in the case of spectra).

The second approach is principal component analysis by projection pursuit, as advocated by several authors. That approach can deal with high-dimensional data but is typically less accurate. Our proposed method, ROBPCA (Robust Principal Component Analysis), combines the advantages of both approaches by being robust, more accurate, and able to handle high-dimensional data.

 Would you summarize the significance of your paper in layman's terms?

Principal component analysis is the most popular technique for data reduction. It transforms data with many variables (columns) to a coordinate system with fewer variables, which often have a meaningful interpretation. Data reduction is extremely useful nowadays, as new data collection methods are widely applied and data storage has become much cheaper.

Consider, for example, microarray data containing gene expressions of thousands of genes, or online process measurements. Unfortunately, the more data are gathered, the more likely it becomes that outliers will be present. Hence, there is a need for statistical methods that perform data reduction while at the same time being robust against outliers, i.e., able to resist the ill effects of outlying cases and able to detect these cases. The proposed method is robust in this sense, and the outlying cases can be detected by the outlier map that is a part of the output.

 Where do you see your research leading in the future?

We continue to do work on constructing robust versions of other statistical techniques, with an eye toward computational feasibility.

Do you foresee any social or political implications for your research?

We don't expect a direct effect, but statistical methods are used in all fields of science (including sociology) as well as in business and for political decision-making. Therefore, developing better statistical tools holds the promise of improving the insights and conclusions of research work done in all these areas.

Professor Mia Hubert
Department of Mathematics
Katholieke Universiteit Leuven
Leuven, Belgium

Professor Peter Rousseeuw
Department of Mathematics and Computer Science
University of Antwerp
Antwerp, Belgium

Karlien Vanden Branden, Ph.D.
Dexia Bank
Brussels, Belgium

Keywords: high-dimensional data sets, spectral data in chemometrics, matlab toolbox libra, robust principal component analysis.

Download this article

back to top


2009 : January 2009 - Fast Moving Fronts : Mia Hubert, Peter Rousseeuw, & Karlien Vanden Branden

  • © 2020 Clarivate
  • Careers
  • Copyright
  • Terms of Use
  • Privacy Policy
  • Cookie Policy
Follow us Share to Twitter Share to LinkedIn Share to Facebook Share to Instagram
Previous
left arrow key
Next
right arrow key
Close Move