  Learning sparse feature representations is a useful instrument for solving an
unsupervised learning problem. In this paper, we present three labeled
handwritten digit datasets, collectively called n-MNIST. Then, we propose a
novel framework for the classification of handwritten digits that learns sparse
representations using probabilistic quadtrees and Deep Belief Nets. On the
MNIST and n-MNIST datasets, our framework shows promising results and
significantly outperforms traditional Deep Belief Networks.
