Discriminant local information distance preserving projection for set classification
Jia Liu · Journal of Tsinghua University(Science and Technology) · 2011
A discriminant local information distance preserving projection(DLIDPP) was developed for the set classification problem.With the DLIDPP assumption,the hidden probability sample distributions lie on a statistical manifold.The Fisher information distance is used as the distance measurement on the statistical manifold.The goal of the DLIDPP is to minimize the information distance between the same class sets as well as to maximize the information distance between neighbouring sets of different classes.A linear mapping matrix is obtained by solving an eigenproblem.Tests on a speaker recognition evaluation data corpus released by the American National Institute of Standards and Technology in 2008 show that the DLIDPP has better recognition than nuissance attirbute projection(NAP) or discriminant nuisance attribute projection(DNAP).