Dimension reduction for model-based clustering via mixtures of multivariate t-distributions

We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of t$$t$$-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. The method relies on identifying a reduced subspace of the data by considering the extent to which group means and group covariances vary. This subspace contains linear combinations of the original data, which are …