  Sparse representation, which uses dictionary atoms to reconstruct input
vectors, has been studied intensively in recent years. A proper dictionary is a
key for the success of sparse representation. In this paper, an active
dictionary learning (ADL) method is introduced, in which classification error
and reconstruction error are considered as the active learning criteria in
selection of the atoms for dictionary construction. The learned dictionaries
are caculated in sparse representation based classification (SRC). The
classification accuracy and reconstruction error are used to evaluate the
proposed dictionary learning method. The performance of the proposed dictionary
learning method is compared with other methods, including unsupervised
dictionary learning and whole-training-data dictionary. The experimental
results based on the UCI data sets and face data set demonstrate the efficiency
of the proposed method.
