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Semi-Supervised Manifold Learning for Hyperspectral Data

: Becker, Florian

Fulltext urn:nbn:de:0011-n-6086876 (851 KByte PDF)
MD5 Fingerprint: 74672c84aa1c7ac2ef7c394cd67e4d60
Created on: 17.11.2020

Beyerer, Jürgen (Ed.); Zander, Tim (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2019. Proceedings : July, 29 to August, 2, 2019, Triberg-Nussbach, Germany
Karlsruhe: KIT Scientific Publishing, 2020 (Karlsruher Schriften zur Anthropomatik 45)
ISBN: 978-3-7315-1028-4
DOI: 10.5445/KSP/1000118012
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2019, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

There are real world data sets where a linear approximation like the principal components might not capture the intrinsic characteristics of the data. Nonlinear dimensionality reduction or manifold learning uses a graph-based approach to model the local structure of the data. Manifold learning algorithms assume that the data resides on a low-dimensional manifold that is embedded in a higher-dimensional space. For real world data sets this assumption might not be evident. However, using manifold learning for a classification task can reveal a better performance than using a corresponding procedure that uses the principal components of the data. We show that this is the case for our hyperspectral dataset using the two manifold learning algorithms Laplacian eigenmaps and locally linear embedding.