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Sparse Bayesian Hierarchical Mixture Of Experts

Publication Type Conference Paper
Authors Iman Mossavat, Oliver Amft
Title Sparse Bayesian Hierarchical Mixture Of Experts
Abstract Hierarchical mixture of experts (HME) is a widely adopted probabilistic divide-and-conquer regression model. We extend the variational inference algorithm for HME by using automatic relevance determination (ARD) priors. Unlike Gaussian priors, ARD allows for a few model parameters to take on large values, while forcing others to zero. Thus, using ARD priors encourages sparse models. Sparsity is known to be advantageous to the generalization capability as well as interpretability of the models. We present the variational inference algorithm for sparse HME in detail. Subsequently, we evaluate the sparse HME approach in building objective speech quality assessment algorithms, that are required to determine the quality of service in telecommunication networks.
Date 2011
Proceedings Title SSP 2011: IEEE International Workshop on Statistical Signal Processing
Publisher IEEE
Pages 653–656
DOI 10.1109/SSP.2011.5967785
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