Abstract
In this paper, we conducted a study of several gradient descent methods namely gradient descent, stochastic gradient descent, momentum method, and AdaGrad for nonlinear mapping of hyperspectral satellite images. The studied methods are compared in terms of both data mapping error and operation time. Two possible applications of the studied methods are considered. First application is the nonlinear dimensionality reduction of the hyperspectral images for the further classification. Another application is the visualization of the hyperspectral images in false colors. The study was carried out using well known hyperspectral satellite images.
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Acknowledgments
This work was financially supported by Russian Foundation for Basic Research, projects no. \(15-07-01164-a\), \(16-37-00202\) mol_a.
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Myasnikov, E. (2016). Evaluation of Stochastic Gradient Descent Methods for Nonlinear Mapping of Hyperspectral Data. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_31
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DOI: https://doi.org/10.1007/978-3-319-41501-7_31
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