Least Square Approach to Out-of-Sample Extensions of Diffusion Maps
Let X = X ∪ Z be a data set in ℝD, where X is the training set and Z the testing one. Assume that a kernel method produces a dimensionality reduction (DR) mapping 𝔉: X → ℝd (d ≪ D) that maps the high-dimensional data X to its row-dimensional representation Y = 𝔉(X). The out-of-sample extension of di...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-05-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fams.2019.00024/full |