Sparse Non-Gaussian Component Analysis

被引：5

作者：

Diederichs, Elmar ^{[1
]}

Juditsky, Anatoli ^{[2
]}

Spokoiny, Vladimir ^{[3
,4
]}

Schuette, Christof ^{[1
]}

机构：

[1] Free Univ Berlin, Inst Math & Informat, D-14195 Berlin, Germany

[2] Univ Grenoble 1, LJK, Grenoble 9, France

[3] Weierstrass Inst, D-10117 Berlin, Germany

[4] Humboldt Univ, D-10117 Berlin, Germany

来源：

IEEE TRANSACTIONS ON INFORMATION THEORY | 2010年 / 56卷 / 06期

关键词：

Convex projection; model reduction; principle component analysis (PCA); reduction of dimensionality; sparsity; structural adaptation; variable selection; REDUCTION;

D O I：

10.1109/TIT.2010.2046229

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3033 / 3047

页数：15

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