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Proceedings Paper

Nonlinear mapping methods with adjustable computational complexity for hyperspectral image analysis
Author(s): E. V. Myasnikov
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Paper Abstract

Nonlinear mapping (Sammon mapping) is a well-known dimensionality reduction technique. Recently several nonlinear mapping methods with reduced computational complexity have been proposed but they do not provide a flexible control over a computational complexity. In this paper a nonlinear mapping method with adjustable computational complexity is proposed. The proposed method is based on the hierarchical decomposition of the multidimensional space, priority queues, and simple optimization procedure to provide fast and flexible dimensionality reduction process. The proposed method is compared to an alternative one based on stochastic optimization. The experiments are carried out on well-known hyperspectral images. Studied methods are evaluated in terms of the data mapping error and runtime. Experimental results for both two- and three-dimensional output spaces are presented.

Paper Details

Date Published: 8 December 2015
PDF: 6 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987508 (8 December 2015); doi: 10.1117/12.2228831
Show Author Affiliations
E. V. Myasnikov, Samara State Aerospace Univ. (Russian Federation)

Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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