Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach

Our focus in this paper is a simple Bayesian classification on generalised Scheiddegger-Watson distribution using standard Maximum Likelihood Estimation (MLE). The main barrier in working with Scheiddegger-Watson or matrix variate distributions via standard MLE is the normalising constant that always appears with them. We apply Taylor expansion for approximating the corresponding matrix-based normalising constant and then implement our proposed approach for classification on Grassmann manifold. We then evaluate the effectiveness of our proposed method on real world data against the state of the art recent techniques and show that the proposed approach outperforms or good comparable with them.