MAP Estimation for Bayesian Mixture Models with Submodular Priors

We propose a Bayesian approach where the signal structure can be represented by a mixture model with a submodular prior. We consider an observation model that leads to Lipschitz functions. Due to its combinatorial nature, computing the maximum a posteriori estimate for this model is NP-Hard, nonetheless our converging majorization-minimization scheme yields approximate estimates that, in practice, outperform state-of-the-art methods.


Presented at:
2014 IEEE International Workshop on Machine Learning for signal processing, Reims, France, Sept 21-24, 2014
Year:
2014
Laboratories:




 Record created 2014-09-23, last modified 2018-10-01

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