Corpus ID: 16765297

# A Linear Approximation to the chi^2 Kernel with Geometric Convergence

@article{Li2012ALA,
title={A Linear Approximation to the chi^2 Kernel with Geometric Convergence},
author={F. Li and G. Lebanon and C. Sminchisescu},
journal={arXiv: Learning},
year={2012}
}
• Published 2012
• Mathematics, Computer Science
• arXiv: Learning
• We propose a new analytical approximation to the $\chi^2$ kernel that converges geometrically. The analytical approximation is derived with elementary methods and adapts to the input distribution for optimal convergence rate. Experiments show the new approximation leads to improved performance in image classification and semantic segmentation tasks using a random Fourier feature approximation of the $\exp-\chi^2$ kernel. Besides, out-of-core principal component analysis (PCA) methods are… CONTINUE READING