PRIMAL-GMM: PaRametrIc MAnifold Learning of Gaussian Mixture Models.

We propose a ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian Mixtures Models (GMM), assuming that GMMs lie on or near to a manifold of probability distributions that is generated from a low-dimensional hierarchical latent space through parametric mappings. Inspired by Principal Component Analysis (PCA), the generative processes for priors, means and covariance matrices are modeled by their respective latent space and parametric...