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A Noise-Robust Fast Sparse Bayesian Learning Model
This paper utilizes the hierarchical model structure from the Bayesian Lasso
in the Sparse Bayesian Learning process to develop a new type of probabilistic
supervised learning approach. The hierarchical model structure in this Bayesian
framework is designed such that the priors do not only penalize the unnecessary
complexity of the model but will also be conditioned on the variance of the
random noise in the data. The hyperparameters in the model are estimated by the
Fast Marginal Likelihood Maximization algorithm which can achieve sparsity, low
computational cost and faster learning process. We compare our methodology with
two other popular learning models; the Relevance Vector Machine and the
Bayesian Lasso. We test our model on examples involving both simulated and
empirical data, and the results show that this approach has several performance
advantages, such as being fast, sparse and also robust to the variance in
random noise. In addition, our method can give out a more stable estimation of
variance of random error, compared with the other methods in the study.Comment: 15 page
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