We investigate three extensions to the generative similarity-based classifier called local similarity discriminant analysis (local SDA): a Bayesian approach to estimating the pmfs based on the assumption that similarities are multinomially distributed and on the Dirichlet prior distribution; a pairwise-similarity formulation of local SDA that accounts for all local pairwise similarities to estimate the pmfs; a combined Bayesian pairwise-similarity approach. We discuss how the proposed extensions afford more modeling flexibility than standard local SDA and less cumbersome model training than previously-published local SDA regularization strategies. Experiments with five benchmark similarity-based classification datasets show that the increased modeling flexibility and lighter computational burden of the proposed extensions are coupled with the good classification performance of the local SDA classification paradigm.