  An increasing number of systems are being designed by gathering significant
amounts of data and then optimizing the system parameters directly using the
obtained data. Often this is done without analyzing the dataset structure. As
task complexity, data size, and parameters all increase to millions or even
billions, data summarization is becoming a major challenge. In this work, we
investigate data summarization via dictionary learning~(DL), leveraging the
properties of recently introduced non-negative kernel regression (NNK) graphs.
Our proposed NNK-Means, unlike previous DL techniques, such as kSVD, learns
geometric dictionaries with atoms that are representative of the input data
space. Experiments show that summarization using NNK-Means can provide better
class separation compared to linear and kernel versions of kMeans and kSVD.
Moreover, NNK-Means is scalable, with runtime complexity similar to that of
kMeans.
