title: Distributed Principal Component Analysis with Limited Communication
abstract: We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. 
  We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case.
authors: Foivos Alimisis Peter Davies Bart Vandereycken Dan Alistarh
hIndex: Dan Alistarh 26