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arXiv · 0904.0430

Sparse NonGaussian Component Analysis

Elmar Diederichs·Anatoli Juditsky·Vladimir Spokoiny·Christof Schuette
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Abstract

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.

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Elmar Diederichs, Anatoli Juditsky, Vladimir Spokoiny, Christof Schuette. 2009-04-24. Sparse NonGaussian Component Analysis. https://arxiv.org/abs/0904.0430

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