A novel dimension reduction procedure for searching non-Gaussian subspaces
- In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new linear method to identify the non-Gaussian subspace. Our method NGCA (Non-Gaussian Component Analysis) is based on a very general semi-parametric framework and has a theoretical guarantee that the estimation error of finding the non-Gaussian components tends to zero at a parametric rate. NGCA can be used not only as preprocessing for ICA, but also for extracting and visualizing more general structures like clusters. A numerical study demonstrates the usefulness of our method
| Author details: | Motoaki Kawanabe, Gilles BlanchardGND, Masashi Sugiyama, Vladimir G. Spokoiny, Klaus-Robert Müller |
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| URL: | http://www.springerlink.com/content/105633/ |
| DOI: | https://doi.org/10.1007/11679363_19 |
| ISSN: | 0302-9743 |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2006 |
| Publication year: | 2006 |
| Release date: | 2017/03/24 |
| Source: | Lecture notes in computer science : independent component analysis and blind signal separation : proceedings. - ISSN 0302-9743. - 3889 (2006), S. 149 - 156 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science |
| Peer review: | Referiert |
| Institution name at the time of the publication: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik |
