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Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficiently. However, the accuracy of such methods can be very sensitive to the choice of regularization parameter, and optimal selection of this parameter remains an open problem. Herein, we propose a maximum a posteriori probability (MAP) approach that investigates different priors on the regularization parameter and yields promising empirical results on both synthetic data and real-life application such as brain imaging data (fMRI).
By: N. Bani Asadi; I. Rish; K. Scheinberg; D. Kanevsky; B. Ramabhadran
Published in: Proceedings of 2009 International Conference on Acoustics, Speech and Signal Processing (ICASSP). , IEEE. , p.1721-4 in 2009
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