Robust subspace mixture models using t-distributions

Probabilistic subspace mixture models, as proposed over the last few years, are interesting methods for learning image manifolds, i.e. nonlinear subspaces of spaces in which images are represented as vectors by their greyvalues. However, for many practical applications, where outliers are common, these methods still lack robustness. Here, the idea of robust mixture modelling by t-distributions is combined with probabilistic subspace mixture models. The resulting robust subspace mixture model is shown experimentally to give advantages in density estimation and classification of image data sets.