Curvilinear Distance Analysis versus Isomap

Lee, John;Lendasse, Amaury;Verleysen, Michel
(2002) European Symposium on Artificial Neural Networks(ESANN′02) — Location: Bruges (Belgium) (24.April.2002)

Files

66-CurvilinearDistanceAnalysisversusIsomap.pdf
  • Restricted Access
  • Adobe PDF
  • 656.76 KB
Request a copy

Details

Authors
Abstract
Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two nonlinear projection methods: Isomap and Curvilinear Distance Analysis. Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pair- wise distances measured in the data space. They dier from the classical linear MDS by the metrics they use and by the way they build the mapping (algebraic or neural). While Isomap relies directly on the traditional MDS, CDA is based on a nonlinear variant of MDS, called CCA (Curvilinear Component Analysis). Although Isomap and CDA share the same metrics, the comparison highlights their respective strengths and weaknesses.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Lee, J., Lendasse, A., & Verleysen, M. (2002). Curvilinear Distance Analysis versus Isomap. Proceedings of ESANN 2002, European Symposium on Artificial Neural Networks, p. 185-192. https://hdl.handle.net/2078.5/225883