A SVM Based on Bhattacharyya Kernel
Xiaomao Liu · Mathematica Applicata · 2005
In various application domians,including image recognition.It is natural to represent each example as a set of vectors.With a base kernel we can implicitly map these vectors to a Hilbert space and fit a Gaussian distribution to the whole set using Kernel PCA.We define our kernel between examples as Bhattacharry's measure of affinity between such Gaussians.The resulting kernel is computable in closed form and enjoys many favorable properties,including graceful behavior under transformations,potentially justifying the vector set representation even in cases when more conventional representations also exist.