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Proceedings Paper

Outlier detection algorithm based on robust component analysis
Author(s): Cha Zheng; Lixin Ji; Chao Gao; Shaomei Li; Yanchuan Wang
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Paper Abstract

In outlier detection problem, most existing algorithms have a notable issue that these approaches cannot detect highdimension outliers effectively. In order to provide a practical solution for this problem, we propose an outlier detection algorithm based on robust component analysis. The basic idea is to train multiple base detectors with the robust component analysis results of the training dataset. Furthermore, we generate some virtual outliers and utilize them to test the capacities of based detectors, and combine them according to the test results to obtain the final outlier detector. Experimental results comparing the proposed method with baseline approaches are presented on several datasets showing the performance of our approach.

Paper Details

Date Published: 26 July 2018
PDF: 7 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280B (26 July 2018); doi: 10.1117/12.2502094
Show Author Affiliations
Cha Zheng, National Digital Switching System Engineering Technological Research Ctr. (China)
Lixin Ji, National Digital Switching System Engineering Technological Research Ctr. (China)
Chao Gao, National Digital Switching System Engineering Technological Research Ctr. (China)
Shaomei Li, National Digital Switching System Engineering Technological Research Ctr. (China)
Yanchuan Wang, National Digital Switching System Engineering Technological Research Ctr. (China)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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