A universal framework for learning based on the elliptical mixture model (EMM).

An increasing prominence of unbalanced and noisy data highlights the importance of elliptical mixture models (EMMs), which exhibit enhanced robustness, flexibility and stability over the widely applied Gaussian mixture model (GMM). However, existing studies of the EMM are typically of \textit{ad hoc} nature, without a universal analysis framework or existence and uniqueness considerations. To this end, we propose a general framework for estimating the EMM, which makes use of the Riemannian manifold optimisation to convert the original constrained optimisation paradigms into an un-constrained one. We first revisit the statistics of elliptical distributions, to give a rationale for the use of Riemannian metrics as well as the reformulation of the problem in the Riemannian space. We then derive the EMM learning framework, based on Riemannian gradient descent, which ensures the same optimum as the original problem but accelerates the convergence speed. We also unify the treatment of the existing elliptical distributions to build a universal EMM, providing a simple and intuitive way to deal with the non-convex nature of this optimisation problem. Numerical results demonstrate the ability of the proposed framework to accommodate EMMs with different properties of individual functions, and also verify the robustness and flexibility of the proposed framework over the standard GMM.