@article{fdi:010082101,
  title = {{R}educed multidimensional scaling},
  author = {{P}aradis, {E}mmanuel},
  editor = {},
  language = {{ENG}},
  abstract = {{D}imension reduction is a common problem when analysing large data sets. {T}he present paper proposes a method called reduced multidimensional scaling based on performing an initial standard multidimensional scaling on a reduced data set. {T}his method faces the problem of finding a representative reduced sample. {A}n algorithm is presented to perform this selection based on alternating sampling in outlier areas and observations in high density areas. {A} space is then constructed with the selected reduced sample by standard multidimentional scaling using pairwise distances. {T}he observations not included in the reduced sample are then projected on the constructed space using {G}ower's formula in order to obtain a final representation of the whole data set. {T}he only requirement is the ability to compute distances among observations. {A} simulation study showed that the proposed algorithm results performs well to detect outliers. {E}valuation of running times suggests that the proposed method could run in a few hours with data sets that would take more than one year to analyse with standard multidimensional scaling. {A}n application is presented with a dataset of 9547 {DNA} sequences of human immunodeficiency viruses.},
  keywords = {{D}imension reduction ; {D}istance data ; {HIV} ; {M}ultidimensional scaling},
  booktitle = {},
  journal = {{C}omputational {S}tatistics},
  volume = {37},
  numero = {},
  pages = {91--105},
  ISSN = {0943-4062},
  year = {2022},
  DOI = {10.1007/s00180-021-01116-0},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082101},
}