Nonlinear Methods for Dimensionality Reduction

Charles K. Chui () and Jianzhong Wang ()
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Charles K. Chui: Stanford University, Department of Statistics
Jianzhong Wang: Sam Houston State University, Department of Mathematics

A chapter in Handbook of Geomathematics, 2015, pp 2799-2851 from Springer

Abstract: Abstract The main objective of this handbook paper is to summarize and compare various popular methods and approaches in the research area of dimensionality reduction of high-dimensional data sets, with emphasis on hyperspectral imagery data. In addition, the topics of our discussions will include data preprocessing, data geometry in terms of similarity/dissimilarity, construction of dimensionality reduction kernels, and dimensionality reduction algorithms based on these kernels.

Keywords: Laplacian Eigenmaps Method; Hessian Locally Linear Embedding (HLLE); Diffusion Maps Method; Local Tangent Space Alignment (LTSA); Isomap Method (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-54551-1_34

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DOI: 10.1007/978-3-642-54551-1_34

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