@inproceedings{11452,
  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.},
  author       = {Alimisis, Foivos and Davies, Peter and Vandereycken, Bart and Alistarh, Dan-Adrian},
  booktitle    = {Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems},
  isbn         = {9781713845393},
  issn         = {1049-5258},
  location     = {Virtual, Online},
  pages        = {2823--2834},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{Distributed principal component analysis with limited communication}},
  volume       = {4},
  year         = {2021},
}