Transposition invariant principal component analysis in L1 for long tailed data
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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Volume (Year): 71 (2005)
Issue (Month): 1 (January)
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References listed on IDEAS
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