Transposition invariant principal component analysis in L1 for long tailed data
AbstractSimilar to the ordinary principal component analysis (PCA), we develop PCA in L1 satisfying an invariance property: The objective function, which is a matrix norm, is transposition invariant. The new method is robust and specifically useful for long-tailed data. An example is provided.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 71 (2005)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Choulakian, V. & Allard, J. & Almhana, J., 2006. "Robust centroid method," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 737-746, November.
- Vartan Choulakian & Jules Tibeiro, 2013. "Graph Partitioning by Correspondence Analysis and Taxicab Correspondence Analysis," Journal of Classification, Springer, vol. 30(3), pages 397-427, October.
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