Optimal Clustering of Multivariate Normal Distributions Using Divergence and Its Application to HMM Adaptation

We present an optimal clustering algorithm for grouping multivariate normal distributions into clusters using the divergence, a symmetric, information-theoretic distortion measure based on the Kullback-Liebler distance. Optimal solutions for normal distributions are shown to be obtained by solving a set of Riccati matrix equations and the optimal centroids are found by alternating the mean and covariance matrix intermediate solutions.