Class Equilibrium using Coulomb's Law
This work addresses a specific problem in machine learning for classification tasks, particularly with low-resolution image data, but appears incremental as it builds on existing projection methods with a physics-inspired adaptation.
The paper tackles the challenge of maintaining optimal inter-class separation without affecting intra-class distance in projection algorithms, proposing a new algorithm inspired by Coulomb's law to compute an equilibrium space for classification, with results showing it performs well for low-resolution images.
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.