Formulating particle swarm optimization based membership linear discriminant analysis

In this paper, we propose the technique for computing the scatter matrices that are used in the linear discriminant analysis using the modified centroids. Modified (weighted) centroids are computed as the weighted mean of all the vectors belonging to the identical cluster. The membership values which are used to compute the weighted centroids are obtained using the fuzzy membership values and the particle swarm optimization. The experiments are performed using the 2-D, 3-D toy clusters and the real database. The experiment results reveal the fact that the detection rate has increased significantly when the classical centroids are replaced with the weighted centroids in LDA.