Distributed low rank approximation of implicit functions of a matrix

@article{Woodruff2016DistributedLR,
  title={Distributed low rank approximation of implicit functions of a matrix},
  author={David P. Woodruff and Peilin Zhong},
  journal={2016 IEEE 32nd International Conference on Data Engineering (ICDE)},
  year={2016},
  pages={847-858}
}
We study distributed low rank approximation in which the matrix to be approximated is only implicitly represented across the different servers. For example, each of s servers may have an n × d matrix A<sup>t</sup>, and we may be interested in computing a low rank approximation to A = f(Σ<sub>t=1</sub><sup>s</sup>A<sup>t</sup>), where f is a function which… CONTINUE READING