  In this work we propose a new deep learning tool called deep dictionary
learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at
a time. This requires solving a simple (shallow) dictionary learning problem,
the solution to this is well known. We apply the proposed technique on some
benchmark deep learning datasets. We compare our results with other deep
learning tools like stacked autoencoder and deep belief network; and state of
the art supervised dictionary learning tools like discriminative KSVD and label
consistent KSVD. Our method yields better results than all.
