Uniqueness of non-Gaussian subspace analysis

被引：0

作者：

Theis, FJ ^{[1
]}

Kawanabe, M

机构：

[1] Univ Regensburg, Inst Biophys, D-93040 Regensburg, Germany

[2] Fraunhofer FIRST IDA, D-12439 Berlin, Germany

来源：

INDEPENDENT COMPONENT ANALYSIS AND BLIND SIGNAL SEPARATION, PROCEEDINGS | 2006年 / 3889卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Dimension reduction provides an important tool for preprocessing large scale data sets. A possible model for dimension reduction is realized by projecting onto the non-Gaussian part of a given multivariate recording. We prove that the subspaces of such a projection are unique given that the Gaussian subspace is of maximal dimension. This result therefore guarantees that projection algorithms uniquely recover the underlying lower dimensional data signals.

引用

页码：917 / 925

页数：9

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