Non-Gaussian component analysis using entropy methods
Navin Goyal, Abhishek S. Shetty · 2019
Non-Gaussian component analysis (NGCA) is a problem in multidimensional data analysis which, since its formulation in 2006, has attracted considerable attention in statistics and machine learning. In this problem, we have a random variable X in n-dimensional Euclidean space. There is an unknown subspace Γ of the n-dimensional Euclidean space such that the orthogonal projection of X onto Γ is standard multidimensional Gaussian and the orthogonal projection of X onto Γ⊥, the orthogonal complement of Γ, is non-Gaussian, in the sense that all its one-dimensional marginals are different from the Gaussian in a certain metric defined in terms of moments. The NGCA problem is to approximate the non-Gaussian subspace Γ⊥ given samples of X.