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Nonlinear dimensionality reduction
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=== Data-driven high-dimensional scaling === Data-driven high-dimensional scaling (DD-HDS)<ref>S. Lespinats, M. Verleysen, A. Giron, B. Fertil, DD-HDS: a tool for visualization and exploration of high-dimensional data, IEEE Transactions on Neural Networks 18 (5) (2007) 1265β1279.</ref> is closely related to [[Sammon's mapping]] and curvilinear component analysis except that (1) it simultaneously penalizes false neighborhoods and tears by focusing on small distances in both original and output space, and that (2) it accounts for [[concentration of measure]] phenomenon by adapting the weighting function to the distance distribution.
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