  Deep Belief Networks (DBN) have been successfully applied on popular machine
learning tasks. Specifically, when applied on hand-written digit recognition,
DBNs have achieved approximate accuracy rates of 98.8%. In an effort to
optimize the data representation achieved by the DBN and maximize their
descriptive power, recent advances have focused on inducing sparse constraints
at each layer of the DBN. In this paper we present a theoretical approach for
sparse constraints in the DBN using the mixed norm for both non-overlapping and
overlapping groups. We explore how these constraints affect the classification
accuracy for digit recognition in three different datasets (MNIST, USPS, RIMES)
and provide initial estimations of their usefulness by altering different
parameters such as the group size and overlap percentage.
