Sparse non-Gaussian component analysis

Authors:
Elmar Diederichs;Anatoli Juditsky;Vladimir Spokoiny;Christof Schütte
Affiliations:
Institute for Mathematics and Informatics, Free University Berlin, Germany;LJK, Université J. Fourier, Grenoble cedex 9, France;theWeierstrass Institute and Humboldt University, Berlin, Germany;Institute for Mathematics and Informatics, Free University Berlin, Germany
Venue:
IEEE Transactions on Information Theory
Year:
2010

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Abstract

Non-Gaussian component analysis (NGCA) introduced in [24] 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.