Fast cross-validation of high-breakdown resampling methods for PCA
Computational Statistics & Data Analysis
Effect of packing on the cluster nature of C nanotubes: An information entropy analysis
Microelectronics Journal
Research Article: Robust data imputation
Computational Biology and Chemistry
Robust PCA and clustering in noisy mixtures
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Robust PCA for skewed data and its outlier map
Computational Statistics & Data Analysis
Efficient computation of PCA with SVD in SQL
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Computational Biology and Chemistry
Robust classification for skewed data
Advances in Data Analysis and Classification
A Weighted Principal Component Analysis and Its Application to Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust prediction of protein subcellular localization combining PCA and WSVMs
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Enhanced classification for high-throughput data with an optimal projection and hybrid classifier
International Journal of Data Mining and Bioinformatics
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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. Results: First, we propose a robust PCA (ROBPCA) method for high-dimensional data. It combines projection-pursuit ideas with robust estimation of low-dimensional data. We also propose a diagnostic plot to display and classify the outliers. This ROBPCA method is applied to several bio-chemical datasets. In one example, we also apply a robust discriminant method on the scores obtained with ROBPCA. We show that this combination of robust methods leads to better classifications than classical PCA and quadratic discriminant analysis. Availability: All the programs are part of the Matlab Toolbox for Robust Calibration, available at http://www.wis.kuleuven.ac.be/stat/robust.html.