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.
CITATION STYLE
K.Chui, C., & Wang, J. (2013). Nonlinear Methods for Dimensionality Reduction. In Handbook of Geomathematics (pp. 1–46). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-27793-1_34-2
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