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This paper presents a Bayesian learning approach for embedded feature selection. This approach employs a fully Bayesian framework to achieve a model which is sparse in both sample and feature domains. We introduce a novel multi-step algorithm based on Variational Approximation to efficiently compute all model parameters in order to optimize the maximum a posteriori probability (MAP) measure. Experiments...
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