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- Title
Principle Component Analysis Based on New Symmetric Similarity Measures for Heavy-Tailed Data.
- Authors
Seidpisheh, Mohammad; Mohammadpour, Adel
- Abstract
We consider the principal component analysis (PCA) for the heavy-tailed distributions. A traditional measure for the classical PCA is the covariance measure. Due to the non-existence of variance of many heavy-tailed distributions, this measure cannot be used for them. We will clarify how to perform PCA in heavy-tailed data by extending a similarity measure based on covariance. We introduce similarity measures based on a new dependence coefficient of heavy-tailed distributions. Using real and artificial datasets, the performance of the proposed PCA is evaluated and compared with the classical one.
- Subjects
PRINCIPAL components analysis; NUMERICAL solutions to functional equations; SYMMETRIC-key algorithms; STATISTICAL correlation; SIGNAL processing; INFORMATION theory
- Publication
Fluctuation & Noise Letters, 2018, Vol 17, Issue 4, pN.PAG
- ISSN
0219-4775
- Publication type
Article
- DOI
10.1142/S0219477518500293