Which Fourier Components Are Most Informative: General Idea and Case Studies

Authors

Julio Urenda, The University of Texas at El PasoFollow
Nancy Avila, The University of Texas at El PasoFollow
Nelly Gordillo, Universidad Autónoma de Ciudad JuárezFollow
Vladik Kreinovich, The University of Texas at El PasoFollow

Publication Date

3-2019

Comments

Technical Report: UTEP-CS-19-21

To appear in Journal of Uncertain Systems, 2019, Vol. 13.

Abstract

In many practical situations, the information comes not in terms of the original image or signal, but in terms of its Fourier transform. To detect complex features based on this information, it is often necessary to use machine learning. In the Fourier transform, usually, there are many components, and it is not easy to use all of them in machine learning. So, we need to select the most informative components. In this paper, we provide general recommendations on how to select such components. We also show that these recommendations are in good accordance with two examples: the structure of the human color vision, and classification of lung dysfunction in children.