  Machine learning problems involving sparse datasets may benefit from the use
of convolutional neural networks if the numbers of samples and features are
very large. Such datasets are increasingly more frequently encountered in a
variety of different domains. We convert such datasets to imagesets while
attempting to give each image structure that is amenable for use with
convolutional neural networks. Experimental results on two publicly available,
sparse datasets show that the approach can boost classification performance
compared to other methods, which may be attributed to the formation of visually
distinguishable shapes on the resultant images.
