Probability-Generating Function Kernels for Spherical Data
Machine Learning
2024-02-02 v2 Machine Learning
Abstract
Probability-generating function (PGF) kernels are introduced, which constitute a class of kernels supported on the unit hypersphere, for the purposes of spherical data analysis. PGF kernels generalize RBF kernels in the context of spherical data. The properties of PGF kernels are studied. A semi-parametric learning algorithm is introduced to enable the use of PGF kernels with spherical data.
Keywords
Cite
@article{arxiv.2112.00365,
title = {Probability-Generating Function Kernels for Spherical Data},
author = {Theodore Papamarkou and Alexey Lindo},
journal= {arXiv preprint arXiv:2112.00365},
year = {2024}
}