In this paper, we present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers. This method is based on the Stochastic v...
Robust clustering of data into overlapping linear subspaces is a common problem. Here we consider one-dimensional subspaces that cross the origin. This problem arises in blind sour...
Finite mixtures of parametric distributions are often used to model data of which it is known or suspected that there are subpopulations. Instead of a parametric model, a penalize...
Document clustering has many uses in natural language tools and applications. For instance, summarizing sets of documents that all describe the same event requires first identifyi...
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...