  In dictionary selection, several atoms are selected from finite candidates
that successfully approximate given data points in the sparse representation.
We propose a novel efficient greedy algorithm for dictionary selection. Not
only does our algorithm work much faster than the known methods, but it can
also handle more complex sparsity constraints, such as average sparsity. Using
numerical experiments, we show that our algorithm outperforms the known methods
for dictionary selection, achieving competitive performances with dictionary
learning algorithms in a smaller running time.
