  Many natural signals exhibit a sparse representation, whenever a suitable
describing model is given. Here, a linear generative model is considered, where
many sparsity-based signal processing techniques rely on such a simplified
model. As this model is often unknown for many classes of the signals, we need
to select such a model based on the domain knowledge or using some exemplar
signals. This paper presents a new exemplar based approach for the linear model
(called the dictionary) selection, for such sparse inverse problems. The
problem of dictionary selection, which has also been called the dictionary
learning in this setting, is first reformulated as a joint sparsity model. The
joint sparsity model here differs from the standard joint sparsity model as it
considers an overcompleteness in the representation of each signal, within the
range of selected subspaces. The new dictionary selection paradigm is examined
with some synthetic and realistic simulations.
