Principal component analysis for compositional data vectors

Huiwen Wang (), Liying Shangguan (), Rong Guan () and Lynne Billard ()

Computational Statistics, 2015, vol. 30, issue 4, 1079-1096

Abstract: Since Aitchison’s founding research work, compositional data analysis has attracted growing attention in recent decades. As a powerful technique for exploratory analysis, principal component analysis (PCA) has been extended to compositional data. Despite extensive efforts in PCA on compositional data parts as variables, this paper contributes to modeling PCA for compositional data vectors. Based on algebraic operators in Simplex space, the PCA process is deduced and transformed into calculating some inner products. Properties of principal components are also investigated. Two real-data examples illustrate the merits of the proposed PCA for compositional data vectors. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Compositional data; Principal component analysis (PCA); Simplex space; Logratio transformation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00180-015-0570-1

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