  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.
