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Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-05-09
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Benefiting from the kernel skill and the sparse property, the relevance vector machine (RVM) could acquire a sparse solution, with an equivalent generalization ability compared with the support vector machine.
The sparse property requires much less time in the prediction, making RVM potential in classifying the large-scale hyperspectral image.
However, RVM is not widespread influenced by its slow training procedure.
To solve the problem, the classification of the hyperspectral image using RVM is accelerated by the parallel computing technique in this paper.
The parallelization is revealed from the aspects of the multiclass strategy, the ensemble of multiple weak classifiers, and the matrix operations.
The parallel RVMs are implemented using the C language plus the parallel functions of the linear algebra packages and the message passing interface library.
The proposed methods are evaluated by the AVIRIS Indian Pines data set on the Beowulf cluster and the multicore platforms.
It shows that the parallel RVMs accelerate the training procedure obviously.
American Psychological Association (APA)
Dong, Chao& Tian, Lianfang. 2012. Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing. Mathematical Problems in Engineering،Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-1001444
Modern Language Association (MLA)
Dong, Chao& Tian, Lianfang. Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing. Mathematical Problems in Engineering No. 2012 (2012), pp.1-13.
https://search.emarefa.net/detail/BIM-1001444
American Medical Association (AMA)
Dong, Chao& Tian, Lianfang. Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing. Mathematical Problems in Engineering. 2012. Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-1001444
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1001444