Reviews: Robust Subspace Approximation in a Stream

This paper studies the use of oblivious dimensionality reduction for approximating a point set with a subspace. It gives a sketching and solve algorithm that reduces both the number of points, and the dimension that they are in, to numbers close to the dimensional of the goal subspace. It then empirically demonstrates that the proposed methods perform better than SVDs. While subspace approximation is important, I'm doubtful of the value of this work for several reasons. First, unlike previous results on oblivious subspace embeddings that introduced new tools, this paper appears to be almost entirely applying existing tools to a mathematically natural variant of the problem. It does not discuss the connections and applications related to this problem.