Greedy Deep Dictionary Learning
This work addresses the problem of improving deep learning performance for researchers and practitioners, but it appears incremental as it builds on existing shallow dictionary learning techniques.
The authors tackled the problem of deep learning by proposing a new deep dictionary learning method that learns multi-level dictionaries greedily, one layer at a time, and applied it to benchmark datasets, yielding better results than other deep learning and supervised dictionary learning tools.
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.