ROBPCA: A New Approach to Robust Principal Component Analysis

By Mia Hubert, Peter J. Rousseeuw, K. V. Branden. 2005

1,086 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Statistics and Probability

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Citations in the corpus

Principal Components and Regularized Estimation of Factor Models (2017, background): “For SPC, see Jolliffee et al. (2003), Ma (2013), Shen and Huang (2008), and Zou et al.”
Principal Component Analysis on Spatial Data: An Overview (2013, neutral): “O, P, Q, R, S, and T—each of which (Hubert, Rousseeuw, and Vanden Branden 2005). addresses a different combination of time, objects, and Many robust PCA techniques are computationally attributes (Richman 1986).”
Bootstrap based confidence limits in principal component analysis: a case study (2013, methodology): “Another possible way to suppress the influence of outliers is to apply (computationally demanding) robust PCA [35,37–39], which is however outside the scope of this paper on bootstrap-based uncertainty estimation.”
An Object-Oriented Framework for Robust Multivariate Analysis (2009, positive): “The next large group of classes are the methods for robust principal component analysis (PCA) including ROBPCA of Hubert et al. (2005), spherical principal components (SPC) of Locantore et al. (1999), the projection pursuit algorithms of Croux and Ruiz-Gazen (2005) and Croux et al. (2007).”
Robust statistics in data analysis — A review: Basic concepts (2007, methodology): “Amore detailed description of the algorithm can be found in [55] and Matlab implementation of this robust PCA method and other robust techniques [61] are available from [62].”
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery (2017, background): “In historical work, it was called just ROBPCA [34].”
Outlier identification in high dimensions (2008, methodology): “Among other advances, there currently exist a wide variety of statistical models ranging from regression to principal components (e.g. Hubert et al., 2005, among several others) that can incorporate outliers without being unduly influenced, as well as several algorithms that explicitly focus on…”
Online Robust PCA via Stochastic Optimization (2013, methodology): “Thus many techniques have been developed towards robustifying it [12] [4] [21] [22] [7].”
Randomized Matrix Decompositions using R (2016, methodology): “These techniques are concerned with computing robust estimators for the empirical covariance matrix, for instance, see the seminal work by Hubert, Rousseeuw, and Vanden Branden (2005) and Croux and Ruiz-Gazen (2005).”
On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness (2018, neutral): “(2005, p. 64) mention two such examples of failed uses of PCA/FA; namely the Economic Sentiment Indicator and the development of an index of environmental sustainability.”