Probability-Generating Function Kernels for Spherical Data
This work addresses spherical data analysis, a domain-specific problem, but appears incremental as it builds on existing kernel methods.
The authors tackled the problem of analyzing spherical data by introducing probability-generating function (PGF) kernels, which generalize radial basis function (RBF) kernels for this context, and developed a semi-parametric learning algorithm to apply these kernels effectively.
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.