High-Dimension, Low Sample Size Asymptotics of Canonical Correlation Analysis
Statistics Theory
2016-09-14 v2 Statistics Theory
Abstract
An asymptotic behavior of canonical correlation analysis is studied when dimension d grows and the sample size n is fxed. In particular, we are interested in the conditions for which CCA works or fails in the HDLSS situation. This technical report investigates those conditions in a rather simplified setting where there exists one pair of directions in two sets of random variables with non-zero correlation between two sets of scores on them. Proofs and an extensive simulation study supports the findings.
Keywords
Cite
@article{arxiv.1609.02992,
title = {High-Dimension, Low Sample Size Asymptotics of Canonical Correlation Analysis},
author = {Sungwon Lee},
journal= {arXiv preprint arXiv:1609.02992},
year = {2016}
}
Comments
Technical report, typos corrected, wrong entry of mentors removed