Sparse NonGaussian Component Analysis
Statistics Theory
2009-04-24 v2 Statistics Theory
Abstract
Non-gaussian component analysis (NGCA) introduced in offered a method for high dimensional data analysis allowing for identifying a low-dimensional non-Gaussian component of the whole distribution in an iterative and structure adaptive way. An important step of the NGCA procedure is identification of the non-Gaussian subspace using Principle Component Analysis (PCA) method. This article proposes a new approach to NGCA called sparse NGCA which replaces the PCA-based procedure with a new the algorithm we refer to as convex projection.
Keywords
Cite
@article{arxiv.0904.0430,
title = {Sparse NonGaussian Component Analysis},
author = {Elmar Diederichs and Anatoli Juditsky and Vladimir Spokoiny and Christof Schuette},
journal= {arXiv preprint arXiv:0904.0430},
year = {2009}
}