2016年12月3日
An Efficient FPGA Implementation of Mahalanobis Distance-Based Outlier Detection for Streaming Data
Proc. 2016 International Conference on Field-Programmable Technology
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- 開始ページ
- 253
- 終了ページ
- 256
- 記述言語
- 英語
- 掲載種別
- 研究論文（国際会議プロシーディングス）
- 出版者・発行元
- IEEE
With the recent explosive growth of data in the real world, data mining techniques to obtain characteristics and knowledge from big data attract more attention. This paper
focuses on a method to detect outliers in streaming data, and proposes a fast FPGA implementation of outlier detection based on the Mahalanobis distance. The proposed circuit is fully pipelined, and in every clock cycle, a given sample data can be judged as an outlier or not. Experimental evaluation shows that the proposed circuit is 37 times faster than the software implementation of the Mahalanobis distance-based outlier detection.
focuses on a method to detect outliers in streaming data, and proposes a fast FPGA implementation of outlier detection based on the Mahalanobis distance. The proposed circuit is fully pipelined, and in every clock cycle, a given sample data can be judged as an outlier or not. Experimental evaluation shows that the proposed circuit is 37 times faster than the software implementation of the Mahalanobis distance-based outlier detection.
- リンク情報
- ID情報
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- Web of Science ID : WOS:000402988900044