ML Anthology

Improved Multidimensional Scaling Analysis Using Neural Networks with Distance-Error Backpropagation

Lluís Garrido, Sergio Gómez, Jaume Roca
NeCo 1999 pp. 595-600
doi:10.1162/089976699300016584 /neco/1999/garrido1999neco-improved/

Abstract

We show that neural networks, with a suitable error function for back-propagation, can be successfully used for metric multidimensional scaling (MDS) (i.e., dimensional reduction while trying to preserve the original distances between patterns) and are in fact able to outdo the standard algebraic approach to MDS, known as classical scaling.

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Cite

Text

Garrido et al. "Improved Multidimensional Scaling Analysis Using Neural Networks with Distance-Error Backpropagation." Neural Computation, 1999. doi:10.1162/089976699300016584

Markdown

[Garrido et al. "Improved Multidimensional Scaling Analysis Using Neural Networks with Distance-Error Backpropagation." Neural Computation, 1999.](https://mlanthology.org/neco/1999/garrido1999neco-improved/) doi:10.1162/089976699300016584

BibTeX

@article{garrido1999neco-improved,
  title     = {{Improved Multidimensional Scaling Analysis Using Neural Networks with Distance-Error Backpropagation}},
  author    = {Garrido, Lluís and Gómez, Sergio and Roca, Jaume},
  journal   = {Neural Computation},
  year      = {1999},
  pages     = {595-600},
  doi       = {10.1162/089976699300016584},
  volume    = {11},
  url       = {https://mlanthology.org/neco/1999/garrido1999neco-improved/}
}