Non-Gaussian component analysis : new ideas, new proofs, new applications

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664831494.pdf (328.63 KB)

Date

2010

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Advisor

Volume

1501

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Journal

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WIAS Preprints

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Publisher

Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik

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Abstract

In this article, we present new ideas concerning Non-Gaussian Component Analysis (NGCA). We use the structural assumption that a high-dimensional random vector vX can be represented as a sum of two components

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Keywords

Dimension reduction, non-Gaussian components, NGCA, EDR subspace, classification problem, Value at Risk

Keywords GND

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Publication Type

Report

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publishedVersion

URI

https://doi.org/10.34657/2722
 https://oa.tib.eu/renate/handle/123456789/2258

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Mathematik
WIAS Preprints

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