@article{18318,
  abstract     = {Multidimensional scaling (MDS) is a generic name for a family of algorithms that construct a configuration of points in a target metric space from information about inter-point distances measured in some other metric space. Large-scale MDS problems often occur in data analysis, representation and visualization. Solving such problems efficiently is of key importance in many applications.
In this paper we present a multigrid framework for MDS problems. We demonstrate the performance of our algorithm on dimensionality reduction and isometric embedding problems, two classical problems requiring efficient large-scale MDS. Simulation results show that the proposed approach significantly outperforms conventional MDS algorithms.},
  author       = {Bronstein, M. M. and Bronstein, Alexander and Kimmel, R. and Yavneh, I.},
  issn         = {1099-1506},
  journal      = {Numerical Linear Algebra with Applications},
  number       = {2-3},
  pages        = {149--171},
  publisher    = {Wiley},
  title        = {{Multigrid multidimensional scaling}},
  doi          = {10.1002/nla.475},
  volume       = {13},
  year         = {2006},
}