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The K-LLE Algorithm for Nonlinear Dimensionality Reduction of Large-Scale Hyperspectral Data

Hong, Danfeng and Yokoya, Naoto and Zhu, Xiao Xiang (2016) The K-LLE Algorithm for Nonlinear Dimensionality Reduction of Large-Scale Hyperspectral Data. In: Proceedings of WHISPERS 2016, pp. 1-5. IEEE Xplore. WHISPERS 2016, 21-24 Aug 2016, Los Angeles, USA.

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Abstract

This work addresses nonlinear dimensionality reduction by means of locally linear embedding (LLE) for large-scale hyperspectral data. The LLE algorithm depends on spectral decomposition to a great extent, resulting in computational complexity and storage-costing while calculating the embedding of the low-dimensional data, particularly for large-scale hyperspectral data. LLE is not applicable to dimensionality reduction of large-scale hyperspectral data using general personal computers. In this paper, we present a novel method named K-LLE which introduces K-means clustering into LLE to deal with this issue. We firstly utilize K-cluster centers to represent the manifold structure of data instead of all data points, and next regard the K-Cluster centers as a bridge between the manifold structure and all data in order to obtain the low-dimensional representation for each data point without handling the complex spectral decomposition. Finally, classification is explored as a potential application to validate the proposed algorithm. Experimental results on two hyperspectral datasets demonstrate the effectiveness and superiority of the proposed algorithm.

Item URL in elib:https://elib.dlr.de/109189/
Document Type:Conference or Workshop Item (Poster)
Title:The K-LLE Algorithm for Nonlinear Dimensionality Reduction of Large-Scale Hyperspectral Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Yokoya, NaotoNaoto.Yokoya (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:2016
Journal or Publication Title:Proceedings of WHISPERS 2016
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-5
Publisher:IEEE Xplore
Status:Published
Keywords:hyperspectral dimensionality reduction, large-scale, manifold learning, K-means clustering
Event Title:WHISPERS 2016
Event Location:Los Angeles, USA
Event Type:international Conference
Event Dates:21-24 Aug 2016
Organizer:IEEE GRSS
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Hong, Danfeng
Deposited On:08 Dec 2016 08:35
Last Modified:31 Jul 2019 20:06

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