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Transposition invariant principal component analysis in L1 for long tailed data


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  • Choulakian, Vartan
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    Similar 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 Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 71 (2005)
    Issue (Month): 1 (January)
    Pages: 23-31

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    Handle: RePEc:eee:stapro:v:71:y:2005:i:1:p:23-31

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    Keywords: PCA Centroid method Transposition invariant matrix norms Transition formulae;


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    Cited by:
    1. Choulakian, V. & Allard, J. & Almhana, J., 2006. "Robust centroid method," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 737-746, November.
    2. 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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