Class-wise deep dictionary learning

In this work we propose a new framework for combined feature extraction and classification. The base idea stems from the sparse representation based classification; where in the training samples from each class are assumed to form a basis for representing the same. Later studies learned a basis for each class using dictionary learning; these were shallow techniques where only one level of dictionary was learnt. In this work we propose to learn multiple levels of dictionaries for each class. We test our technique on benchmark deep learning datasets. We compare our proposed method with deep (stacked autoencoder, deep belief network) techniques and shallow (support vector machine and label consistent dictionary learning) techniques; ours yield the best results overall. We also carry out an empirical analysis with perturbations. We find that our method is more robust compared to other deep learning techniques in the presence of different kinds of noise, missing features and varying amounts of training data.