Rank-based quality assessment of nonlinear dimensionality reduction
(2008) 16th European Symposium on Artificial Neural Networks (ESANN 2008) — Location: Bruges (Belgium) (23.April.2008)
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- Abstract
- Nonlinear dimensionality reduction aims at providing low dimensional representations of high-dimensional data sets. Many new methods have been proposed in the recent years, but the question of their assessment and comparison remains open. This paper reviews some of the existing quality measures that are based on distance ranking and K-ary neighborhoods. Many quality criteria actually rely on the analysis of one or several sub-blocks of a co-ranking matrix. The analogy between the co-ranking matrix and a Shepard diagram is highlighted. Finally, a unifying framework is sketched, new measures are proposed and illustrated in a short experiment.
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Lee, J., & Verleysen, M. (2008). Rank-based quality assessment of nonlinear dimensionality reduction. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN 2008), p. 49-54. https://hdl.handle.net/2078.5/253857