Robust estimation of gaussian mixtures from noisy input data

We propose a variational Bayes approach to the problem of robust estimation of Gaussian mixtures from noisy input data. The proposed algorithm explicitly takes into account the uncertainty associated with each data point, makes no assumptions about the structure of the covariance matrices and is able to automatically determine the number of the Gaussian mixture components. Through the use of both synthetic and real world data examples, we show that by incorporating uncertainty information into the clustering algorithm, we get better results at recovering the true distribution of the training data compared to other variational Bayesian clustering algorithms.