A fast method for robust principal components with applications to chemometrics

By Mia Hubert, Peter J. Rousseeuw, Sabine Verboven. 2002

251 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8c1bd5331c7d1cb19574c2ce4470d96c9d8044a8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Mia Hubert
2. Peter J. Rousseeuw
3. K. V. Branden
4. S. Van Aelst
5. Douglas M. Hawkins
6. David J. Olive
7. Sabine Verboven
8. M. Salibián‐Barrera

In Applied Mathematics

1. Mia Hubert
2. K. V. Branden
3. Peter J. Rousseeuw
4. Douglas M. Hawkins
5. David J. Olive
6. Sabine Verboven
7. S. Van Aelst
8. M. Salibián‐Barrera

In Statistics and Probability

1. Mia Hubert
2. Peter J. Rousseeuw
3. S. Van Aelst
4. K. V. Branden
5. Sabine Verboven
6. Gert Willems
7. David J. Olive
8. Douglas M. Hawkins

Citations in the corpus

• Principal Component Analysis on Spatial Data: An Overview (2013, unknown): “If technique where projections of the data are searched time is added as one of the measurements, resulting in for outliers (Hubert, Rousseeuw, and Verboven 2002), space-time series data, then there are in total six dif-and (3) a mixture of both MCD and a PP technique ferent modes—”
• Principal Components Analysis Based on Multivariate MM Estimators With Fast and Robust Bootstrap (2006, methodology): “Consequently, several robustifications of classical PCA have been proposed in the literature, including projection-pursuit ap proaches (Li and Chen 1985; Croux and Ruiz-Gazen 2005; Hubert, Rousseeuw, and Verboven 2002) and the use of robust estimators of scatter instead of the sample covariance.”
• Increased sensitivity in neuroimaging analyses using robust regression (2005, background): “Although several groups have developed robust versions of multivariate techniques (Hubert and Vanden Branden, 2003; Hubert and Verboven, 2003; Hubert et al., 2002), we focus here on the former, bmassively univariateQ approach.”
• ROBPCA: A New Approach to Robust Principal Component Analysis (2005, methodology): “From the robust diagnostic plot in Figure 5(c), we see that the outliers are still very far from the estimated robust subspace The diagnostic plots of RAPCA, SPHER, and ELL were similar to Figure 5(b) for k = .”
• Inconsistency of Resampling Algorithms for High-Breakdown Regression Estimators and a New Algorithm (2002, methodology): “For this, one could, for example, apply a robust PCA method (see, e.g., Hubert, Rousseeuw, and Verboven 2002) to the xi .”
• A robust PCR method for high‐dimensional regressors (2003, methodology): “To illustrate these concepts, we analyze the Biscuit Dough data set [19, 18] preprocessed as in [13].”
• Robust methods for partial least squares regression (2003, methodology): “For such high-dimensional data sets, projection pursuit algorithms have been developed [12], [7].”