Method evidence record
Robust PCA
Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.
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Robust Principal Component Analysis
Taxonomic method record · regression-model / statistics
- Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37. · DOI 10.1145/1970392.1970395
- Hubert, M., Rousseeuw, P. J., & Vanden Branden, K. (2005). ROBPCA: A New Approach to Robust Principal Component Analysis. Technometrics, 47(1), 64-79. · DOI 10.1198/004017004000000563
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