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Assessment of dimensionality reduction based on communication channel model; application to immersive information visualization

Babaee, Mohammadreza and Datcu, Mihai and Rigoll, Gerald (2013) Assessment of dimensionality reduction based on communication channel model; application to immersive information visualization. In: 2013 IEEE International Conference on Big Data, pp. 1-6. IEEE Xplore. Big Data 2013, 6.-9. Oct. 2013, Silicon Valley, CA, USA. ISBN doi: 10.1109/BigData.2013.6691726

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Official URL: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6679357

Abstract

We are dealing with large-scale high-dimensional image data sets requiring new approaches for data mining where visualization plays the main role. Dimension reduction (DR) techniques are widely used to visualize high-dimensional data. However, the information loss due to reducing the number of dimensions is the drawback of DRs. In this paper, we introduce a novel metric to assess the quality of DRs in terms of preserving the structure of data. We model the dimensionality reduction process as a communication channel model transferring data points from a high-dimensional space (input) to a lower one (output). In this model, a co-ranking matrix measures the degree of similarity between the input and the output. Mutual information (MI) and entropy defined over the co-ranking matrix measure the quality of the applied DR technique. We validate our method by reducing the dimension of SIFT and Weber descriptors extracted from Earth Observation (EO) optical images. In our experiments, Laplacian Eigenmaps (LE) and Stochastic Neighbor Embedding (SNE) act as DR techniques. The experimental results demonstrate that the DR technique with the largest MI and entropy preserves the structure of data better than the others.

Item URL in elib:https://elib.dlr.de/88828/
Document Type:Conference or Workshop Item (Speech)
Title:Assessment of dimensionality reduction based on communication channel model; application to immersive information visualization
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Babaee, MohammadrezaTU MunichUNSPECIFIED
Datcu, Mihaimihai.datcu (at) dlr.deUNSPECIFIED
Rigoll, GeraldTU MunichUNSPECIFIED
Date:2013
Journal or Publication Title:2013 IEEE International Conference on Big Data
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-6
Publisher:IEEE Xplore
ISBN:doi: 10.1109/BigData.2013.6691726
Status:Published
Keywords:Dimensionality Reduction; Immersive information Visualization; Communication channel; Quality Assessment
Event Title:Big Data 2013
Event Location:Silicon Valley, CA, USA
Event Type:international Conference
Event Dates:6.-9. Oct. 2013
Organizer:IEEE
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 > Photogrammetry and Image Analysis
Deposited By:INVALID USER
Deposited On:14 Apr 2014 15:07
Last Modified:31 Jul 2019 19:46

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