Sparse Non Gaussian Component Analysis by Semidefinite Programming
Statistics Theory
2012-01-17 v3 Optimization and Control
Computation
Machine Learning
Statistics Theory
Abstract
Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target space based on semidefinite programming which improves the method sensitivity to a broad variety of deviations from normality. We also discuss the procedures which allows to recover the structure when its effective dimension is unknown.
Keywords
Cite
@article{arxiv.1106.0321,
title = {Sparse Non Gaussian Component Analysis by Semidefinite Programming},
author = {Elmar Diederichs and Anatoli Juditsky and Arkadi Nemirovski and Vladimir Spokoiny},
journal= {arXiv preprint arXiv:1106.0321},
year = {2012}
}