  Matrices have become essential data representations for many large-scale
problems in data analytics, and hence matrix sketching is a critical task.
Although much research has focused on improving the error/size tradeoff under
various sketching paradigms, the many forms of error bounds make these
approaches hard to compare in theory and in practice. This paper attempts to
categorize and compare most known methods under row-wise streaming updates with
provable guarantees, and then to tweak some of these methods to gain practical
improvements while retaining guarantees.
  For instance, we observe that a simple heuristic iSVD, with no guarantees,
tends to outperform all known approaches in terms of size/error trade-off. We
modify the best performing method with guarantees FrequentDirections under the
size/error trade-off to match the performance of iSVD and retain its
guarantees. We also demonstrate some adversarial datasets where iSVD performs
quite poorly. In comparing techniques in the time/error trade-off, techniques
based on hashing or sampling tend to perform better. In this setting we modify
the most studied sampling regime to retain error guarantee but obtain dramatic
improvements in the time/error trade-off.
  Finally, we provide easy replication of our studies on APT, a new testbed
which makes available not only code and datasets, but also a computing platform
with fixed environmental settings.
