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
Chui, C. K., & Wang, J. (2015). Nonlinear methods for dimensionality reduction. In Handbook of Geomathematics: Second Edition (pp. 2799–2851). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-54551-1_34
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