Transposition invariant principal component analysis in L1 for long tailed data

Vartan Choulakian

Statistics & Probability Letters, 2005, vol. 71, issue 1, 23-31

Abstract: 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.

Keywords: PCA; Centroid; method; Transposition; invariant; matrix; norms; Transition; formulae (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (5)

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