Stochastic Neighborhood Embedding

Wolfgang Karl Härdle, Leopold Simar and Matthias Fengler
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Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, Ladislaus von Bortkiewicz Chair of Statistics

Chapter Chapter 22 in Applied Multivariate Statistical Analysis, 2024, pp 569-580 from Springer

Abstract: Abstract LLE is widely appreciated as an effective dimension-reduction tool. It fares less well, however, in situations of very high-dimensional data sets. Moreover, an essential assumption of LLE is the presence of a single smooth manifold in the data. If the data contain multiple manifolds or if there are regions, where the density of observed data varies a lot, LLE has performance weaknesses.

Date: 2024
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DOI: 10.1007/978-3-031-63833-6_22

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