Sparse Non Gaussian Component Analysis by Semidefinite Programming

TL;DR

This paper introduces a semidefinite programming approach for sparse non-Gaussian component analysis, enhancing the detection of non-Gaussian structures in high-dimensional data and addressing unknown structural dimensions.

Contribution

It proposes a novel semidefinite programming method for direct estimation of the target space projector in SNGCA, improving sensitivity to non-Gaussian deviations and handling unknown dimensions.

Findings

Enhanced sensitivity to non-Gaussian deviations

Effective estimation of the target space projector

Ability to recover structure with unknown dimension

Abstract

Sparse non-Gaussian component analysis (SNGCA) is an unsupervised method of extracting a linear structure from a high dimensional data based on estimating a low-dimensional non-Gaussian data component. In this paper we discuss a new approach to direct estimation of the projector on the target space based on semidefinite programming which improves the method sensitivity to a broad variety of deviations from normality. We also discuss the procedures which allows to recover the structure when its effective dimension is unknown.