Extended Expectation Maximization for Under-Fitted Models

In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using the α-divergence for Gaussian Mixture Model (GMM). This approach is used in robust subspace detection when the number of parameters is kept small to avoid overfitting and large estimation variances. The level of robustness can be tuned by the parameter α. When α → 1, our method is equivalent to the standard EM approach and for α < 1 the method is robust against potential outliers. Simulation results show that the method outperforms the standard EM when it comes to mismatches between noise models and their realizations. In addition, we use the proposed method to detect active brain areas using collected functional Magnetic Resonance Imaging (fMRI) data during task-related experiments.