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 grey-values. Their lack of a global mapping can be remedied by a recently developed method based on locally linear embedding, called locally linear coordination. However, for many practical applications, where outliers are common, this method lacks the necessary 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. It also solves the robustness problems of locally linear co-ordination, by introducing a weighted reformulation of the embedding step.
