An efficient implementation of Probabilistic Linear Discriminant Analysis

Probabilistic Linear Discriminant Analysis (PLDA), used particularly in image and speech processing for face and speaker recognition, respectively, is a generative model requesting lots of data to be trained. In the paper several enhancements concerning the implementation of the estimation algorithm of PLDA are proposed providing substantial computational savings. At first, an inverse of a huge matrix is replaced by an inversion of two significantly smaller matrices. Subsequently, it is shown how to avoid the need to process the whole data set in each iteration of the estimation algorithm. Supplementary results are presented on NIST SRE 2008.