Multi-scale kernels for Nyström based extension schemes

N. Rabin and D. Fishelov

Applied Mathematics and Computation, 2018, vol. 319, issue C, 165-177

Abstract: Nonlinear dimensionality reduction methods often include the construction of kernels for embedding the high-dimensional data points. Standard methods for extending the embedding coordinates (such as the Nyström method) also rely on spectral decomposition of kernels. It is desirable that these kernels capture most of the data sets’ information using only a few leading modes of the spectrum.

Keywords: Kernel methods; Manifold learning; Dimensionality reduction; Function extension (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:319:y:2018:i:c:p:165-177

DOI: 10.1016/j.amc.2017.02.025

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