@inproceedings{b645b09f6da84aaea2349c3836fe5acc,
title = "Estimating non-Gaussian subspaces by characteristic functions",
abstract = "In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new method to identify the non-Gaussian subspace. A linear dimension reduction algorithm based on the fourth-order cumulant tensor was proposed in our previous work [4]. Although it works well for sub-Gaussian structures, the performance is not satisfactory for super-Gaussian data due to outliers. To overcome this problem, we construct an alternative by using Hessian of characteristic functions which was applied to (multidimensional) independent component analysis [10,11]. A numerical study demonstrates the validity of our method.",
author = "Motoaki Kawanabe and Theis, {Fabian J.}",
year = "2006",
doi = "10.1007/11679363_20",
language = "English",
isbn = "3540326308",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "157--164",
booktitle = "Independent Component Analysis and Blind Signal Separation - 6th International Conference, ICA 2006, Proceedings",
note = "6th International Conference on Independent Component Analysis and Blind Signal Separation, ICA 2006 ; Conference date: 05-03-2006 Through 08-03-2006",
}