Non-Gaussian component analysis: testing the dimension of the signal subspace

TL;DR

This paper introduces a method for identifying the dimension of the non-Gaussian signal subspace in multivariate data by using two scatter functionals and a bootstrap test, aiding in effective dimension reduction.

Contribution

It proposes a novel approach combining two scatter functionals with a bootstrap test to determine the non-Gaussian subspace dimension in data analysis.

Findings

Effective bootstrap test for subspace dimension

Method for estimating signal dimension sequentially

Improved separation of signal and noise components

Abstract

Dimension reduction is a common strategy in multivariate data analysis which seeks a subspace which contains all interesting features needed for the subsequent analysis. Non-Gaussian component analysis attempts for this purpose to divide the data into a non-Gaussian part, the signal, and a Gaussian part, the noise. We will show that the simultaneous use of two scatter functionals can be used for this purpose and suggest a bootstrap test to test the dimension of the non-Gaussian subspace. Sequential application of the test can then for example be used to estimate the signal dimension.