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首页> 外文期刊>Mathematical Problems in Engineering >Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing
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Accelerating Relevance-Vector-Machine-Based Classification of Hyperspectral Image with Parallel Computing

机译：并行计算加速基于相关向量机的高光谱图像分类

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摘要

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.
机译：受益于内核技能和稀疏属性，相关向量机（RVM）可以获得稀疏解，与支持向量机相比具有同等的泛化能力。稀疏属性在预测中所需的时间要少得多，这使得RVM在分类大型高光谱图像方面具有潜力。但是，RVM的缓慢训练过程并未对其产生广泛的影响。为了解决该问题，本文通过并行计算技术加快了使用RVM对高光谱图像的分类。从多类策略，多个弱分类器的集合以及矩阵运算的方面揭示了并行化。并行RVM使用C语言以及线性代数包和消息传递接口库的并行功能来实现。通过Beowulf集群和多核平台上的AVIRIS Indian Pines数据集对提出的方法进行了评估。结果表明，并行RVM明显加快了训练过程。

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  • 来源
    《Mathematical Problems in Engineering》 |2012年第5期|p.28.1-28.13|共13页
  • 作者

    Chao Dong; Lianfang Tian;

  • 作者单位

    Key Laboratory of Autonomous Systems and Network Control of the Ministry of Education, School of Automation Science and Engineering, South China University of Technology, Wushan Road No.381, Tianhe District, Guangzhou 510641, China;

    Key Laboratory of Autonomous Systems and Network Control of the Ministry of Education, School of Automation Science and Engineering, South China University of Technology, Wushan Road No.381, Tianhe District, Guangzhou 510641, China;

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