Sparse NonGaussian Component Analysis
| dc.creator | Diederichs, Elmar | |
| dc.creator | Juditsky, Anatoli | |
| dc.creator | Spokoiny, Vladimir | |
| dc.creator | Schuette, Christof | |
| dc.date | 2009-04-02 | |
| dc.date | 2009-04-24 | |
| dc.date.accessioned | 2026-07-07T13:07:48Z | |
| dc.date.available | 2026-07-07T13:07:48Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/0904.0430 | |
| dc.identifier | http://arxiv.org/abs/0904.0430 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/228213 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62G05, 60G10, 60G35, 62M10, 93E10 | |
| dc.title | Sparse NonGaussian Component Analysis | |
| dc.type | text |