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/ | ||||||||||||
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Document Type: | Conference or Workshop Item (Poster) | ||||||||||||
Title: | The K-LLE Algorithm for Nonlinear Dimensionality Reduction of Large-Scale Hyperspectral Data | ||||||||||||
Authors: |
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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 - Earth Observation | ||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | ||||||||||||
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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