  Currently there are two predominant ways to train deep neural networks. The
first one uses restricted Boltzmann machine (RBM) and the second one
autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the
final representation layer is attached to the target to complete the deep
neural network. Autoencoders are nested one inside the other to form stacked
autoencoders; once the stcaked autoencoder is learnt the decoder portion is
detached and the target attached to the deepest layer of the encoder to form
the deep neural network. This work proposes a new approach to train deep neural
networks using dictionary learning as the basic building block; the idea is to
use the features from the shallower layer as inputs for training the next
deeper layer. One can use any type of dictionary learning (unsupervised,
supervised, discriminative etc.) as basic units till the pre-final layer. In
the final layer one needs to use the label consistent dictionary learning
formulation for classification. We compare our proposed framework with existing
state-of-the-art deep learning techniques on benchmark problems; we are always
within the top 10 results. In actual problems of age and gender classification,
we are better than the best known techniques.
