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Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data

Bradley, Patrick Erik ORCID iD icon 1; Keller, Sina 1; Weinmann, Martin 1
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)


In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000086217
Veröffentlicht am 04.10.2018
DOI: 10.3390/rs10101564
Zitationen: 11
Web of Science
Zitationen: 7
Zitationen: 12
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000086217
Erschienen in Remote sensing
Verlag MDPI
Band 10
Heft 10
Seiten Art.Nr.: 1564
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Vorab online veröffentlicht am 29.09.2018
Schlagwörter unsupervised feature selection; ultrametricity; sparse training data; classification; land cover; land use; hyperspectral imagery; ROSIS data; AVIRIS data; EnMAP data
Nachgewiesen in Web of Science
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