Distilled Gaussianization

James Theiler; Christopher X. Ren

doi:10.1117/12.2594598

1 August 2021 Distilled Gaussianization

James Theiler, Christopher X. Ren

Proceedings Volume 11843, Applications of Machine Learning 2021; 118430G (2021) https://doi.org/10.1117/12.2594598
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Abstract

Gaussianization is a recently suggested approach for density estimation from data drawn from a decidedly non-Gaussian, and possibly high dimensional, distribution. The key idea is to learn a transformation that, when applied to the data, leads to an approximately Gaussian distribution. The density, for any given point in the original distribution, is then given by the determinant of the transformation's Jacobian at that point, multiplied by the (analytically known) density of the Gaussian for the transformed data. In this work, we investigate the use of distilled machine learning to provide a compact implementation of the Gaussianization transform (which in usual practice is obtained iteratively), thereby enabling faster computation, better controlled regularization, and more direct estimation of the Jacobian. While density estimation underlies many statistical analyses, our interest is in hyperspectral detection problems.

Conference Presentation

Citation Download Citation

James Theiler and Christopher X. Ren "Distilled Gaussianization", Proc. SPIE 11843, Applications of Machine Learning 2021, 118430G (1 August 2021); https://doi.org/10.1117/12.2594598

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