A framework of using t mixture models with fourteen eigen-decomposed covariance structures for the unsupervised learning of heterogeneous multivariate data with possible missing values is designed and implemented. Computationally flexible EM-type algorithms are developed for parameter estimation...

An approach is proposed for initializing the expectation–maximization (EM) algorithm in multivariate Gaussian mixture models with an unknown number of components. As the EM algorithm is often sensitive to the choice of the initial parameter vector, efficient initialization is an important...