Supervised Bayesian sparse coding for classification
Jinhua Xu, Li Ding, Shiliang Sun · 2014
In this paper, we propose a supervised Bayesian sparse coding (SBSC) model for classification. The sparse coding with Laplacian scale mixture prior is formulated as a weighted l1minimization problem. Category-specific discriminative dictionaries and regularization parameters are learned using variational EM algorithm from the training samples of each category. Instability of previous sparse coding methods is alleviated through the regularizer design. Classification of a test sample is done using the MAP estimate of the sparse codes. We have tested the model on different recognition tasks and demonstrated the effectiveness of the model.