Class Equilibrium using Coulomb's Law

TL;DR

This paper introduces a novel algorithm inspired by Coulomb's law to achieve optimal class separation in a transformed equilibrium space, improving classification especially on low-resolution images.

Contribution

The paper proposes a new Coulomb's law-based algorithm to compute an equilibrium space for optimal class separation without affecting intra-class distances.

Findings

Performs well on low-resolution images

Achieves optimal inter-class separation

Effective across multiple datasets

Abstract

Projection algorithms learn a transformation function to project the data from input space to the feature space, with the objective of increasing the inter-class distance. However, increasing the inter-class distance can affect the intra-class distance. Maintaining an optimal inter-class separation among the classes without affecting the intra-class distance of the data distribution is a challenging task. In this paper, inspired by the Coulomb's law of Electrostatics, we propose a new algorithm to compute the equilibrium space of any data distribution where the separation among the classes is optimal. The algorithm further learns the transformation between the input space and equilibrium space to perform classification in the equilibrium space. The performance of the proposed algorithm is evaluated on four publicly available datasets at three different resolutions. It is observed that the proposed algorithm performs well for low-resolution images.