1 research outputs found
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification
In recent studies in hyperspectral imaging, biometrics and energy analytics,
the framework of deep dictionary learning has shown promise. Deep dictionary
learning outperforms other traditional deep learning tools when training data
is limited; therefore hyperspectral imaging is one such example that benefits
from this framework. Most of the prior studies were based on the unsupervised
formulation; and in all cases, the training algorithm was greedy and hence
sub-optimal. This is the first work that shows how to learn the deep dictionary
learning problem in a joint fashion. Moreover, we propose a new discriminative
penalty to the said framework. The third contribution of this work is showing
how to incorporate stochastic regularization techniques into the deep
dictionary learning framework. Experimental results on hyperspectral image
classification shows that the proposed technique excels over all
state-of-the-art deep and shallow (traditional) learning based methods
published in recent times.Comment: Accepted at IEEE JSTAR