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Nonparametric Bayesian models have been implemented in dictionary learning. However, for signal samples from multiple subspaces, existing methods only learn one uniform dictionary and thus are not optimal for representing the subspace structures. To address this issue, we first utilize a combination of Dirichlet process and hierarchical Beta process as priors to infer the latent subspace number and...
Nonparametric Bayesian approach is considered for learning appropriate dictionaries in sparse image representations. However, for images from multiple separate sources, existing methods have two issues that potentially limit their practical implements: first, learning one unified dictionary is not optimal for representing image samples in different subspaces; second, the required number of dictionaries...
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