Sparse Non-Gaussian Component Analysis

Author

Diederichs, Elmar ; Juditsky, Anatoli ; Spokoiny, Vladimir ; Schütte, Christof

Author_Institution

Inst. for Math. & Inf., Free Univ. Berlin, Berlin, Germany

Volume

56

Issue

6

fYear

2010

fDate

6/1/2010 12:00:00 AM

Firstpage

3033

Lastpage

3047

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.

Keywords

Gaussian noise; iterative methods; principal component analysis; sparse matrices; convex projection; high-dimensional data analysis; iterative way; nonGaussian subspace; principle component analysis; sparse nonGaussian component analysis; structure adaptive way; Biology computing; Data analysis; Gaussian noise; Input variables; Iterative algorithms; Iterative methods; Least squares approximation; Principal component analysis; Reduced order systems; Statistical analysis; Convex projection; model reduction; principle component analysis (PCA); reduction of dimensionality; sparsity; structural adaptation; variable selection;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/TIT.2010.2046229

Filename

5466518