  We study the distributed computing setting in which there are multiple
servers, each holding a set of points, who wish to compute functions on the
union of their point sets. A key task in this setting is Principal Component
Analysis (PCA), in which the servers would like to compute a low dimensional
subspace capturing as much of the variance of the union of their point sets as
possible. Given a procedure for approximate PCA, one can use it to
approximately solve $\ell_2$-error fitting problems such as $k$-means
clustering and subspace clustering. The essential properties of an approximate
distributed PCA algorithm are its communication cost and computational
efficiency for a given desired accuracy in downstream applications. We give new
algorithms and analyses for distributed PCA which lead to improved
communication and computational costs for $k$-means clustering and related
problems. Our empirical study on real world data shows a speedup of orders of
magnitude, preserving communication with only a negligible degradation in
solution quality. Some of these techniques we develop, such as a general
transformation from a constant success probability subspace embedding to a high
success probability subspace embedding with a dimension and sparsity
independent of the success probability, may be of independent interest.

Machine Learning