Curvilinear distance analysis versus Isomap
(2002) Proceedings of ESANN′2002. 10th European Symposium on Artificial Neural Networks — Location: Bruges, Belgium (24.April.2002)
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- Authors
Lee, John Aldo 
- Abstract
- Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two nonlinear projection methods: the Isomap and curvilinear distance analysis (CDA). Contrarily to the traditional linear PCA, these methods work like multidimensional scaling (MDS) by reproducing in the projection space the pairwise distances measured in the data space. They differ 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 the curvilinear component analysis. Although Isomap and CDA share the same metrics, the comparison highlights their respective strengths and weaknesses.
- Affiliations
Citations
Lee, J. A., Lendasse, A., & Verleysen, N. (2002). Curvilinear distance analysis versus Isomap. In Verleysen, M.; (ed.), 10th European Symposium on Artificial Neural Networks. ESANN′2002.Proceedings (p. p. 185-192). D-side publications. https://hdl.handle.net/2078.5/230106