A Class of Kernels for Sets of Vectors
F. Desobry, Manuel Davy, William J. Fitzgerald · 2005
Abstract. In some important applications such as speaker recognition or image texture classification, the data to be processed are sets of vectors. As opposed to standard settings where the data are individual vectors, it is difficult to design a reliable kernel between sets of vectors of possibly different cardinality. In this paper, we build kernels between sets of vectors from probability density functions level sets estimated for each set of vectors, where a pdf level set is (roughly) a part of the space where most of the data lie. 1