Transelliptical Component Analysis
Abstract
We propose a high dimensional semiparametric scale-invariant principle compo- nent analysis, named TCA, by utilize the natural connection between the ellipti- cal distribution family and the principal component analysis. Elliptical distribu- tion family includes many well-known multivariate distributions like multivari- ate Gaussian, t and logistic and it is extended to the meta-elliptical by Fang et.al (2002) using the copula techniques. In this paper we extend the meta-elliptical distribution family to a even larger family, called transelliptical. We prove that
Cite
Text
Han and Liu. "Transelliptical Component Analysis." Neural Information Processing Systems, 2012.Markdown
[Han and Liu. "Transelliptical Component Analysis." Neural Information Processing Systems, 2012.](https://mlanthology.org/neurips/2012/han2012neurips-transelliptical/)BibTeX
@inproceedings{han2012neurips-transelliptical,
title = {{Transelliptical Component Analysis}},
author = {Han, Fang and Liu, Han},
booktitle = {Neural Information Processing Systems},
year = {2012},
pages = {359-367},
url = {https://mlanthology.org/neurips/2012/han2012neurips-transelliptical/}
}