arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1003.1967cs.NI
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无线传感器网络的分布式主元分析

Distributed Principal Component Analysis for Wireless Sensor Networks

Yann-Aël Le Borgne, Sylvain Raybaud, Gianluca Bontempi

摘要:

主成分分析(PCA)是一种数据降维技术,非常适合处理来自传感器网络的数据。它可以应用于压缩、事件检测和事件识别等任务。该技术基于线性变换,其中传感器测量被投影在一组主分量上。当传感器测量值相关时,一小组主成分可以解释大多数测量值的变化。这允许显著减少无线电通信量和能量消耗。在本文中,我们表明,功率迭代方法可以分布在传感器网络中,以计算近似的主成分。所提出的实现依赖于聚合服务,最近已被证明提供了一个合适的框架,用于分布在传感器网络内的线性变换的计算。我们还扩展了以前的工作,提供了一个详细的分析所涉及的计算,内存和通信成本。一个涉及真实数据的压缩实验验证了该算法,并说明了准确性和通信成本之间的权衡。

英文摘要:

The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like compression, event detection, and event recognition. This technique is based on a linear transform where the sensor measurements are projected on a set of principal components. When sensor measurements are correlated, a small set of principal components can explain most of the measurements variability. This allows to significantly decrease the amount of radio communication and of energy consumption. In this paper, we show that the power iteration method can be distributed in a sensor network in order to compute an approximation of the principal components. The proposed implementation relies on an aggregation service, which has recently been shown to provide a suitable framework for distributing the computation of a linear transform within a sensor network. We also extend this previous work by providing a detailed analysis of the computational, memory, and communication costs involved. A compression experiment involving real data validates the algorithm and illustrates the tradeoffs between accuracy and communication costs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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