Outlier detection algorithm based on robust component analysis

Cha Zheng; Lixin Ji; Chao Gao; Shaomei Li; Yanchuan Wang

doi:10.1117/12.2502094

26 July 2018 Outlier detection algorithm based on robust component analysis

Cha Zheng, Lixin Ji, Chao Gao, Shaomei Li, Yanchuan Wang

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108280B (2018) https://doi.org/10.1117/12.2502094
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

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.

Citation Download Citation

Cha Zheng, Lixin Ji, Chao Gao, Shaomei Li, and Yanchuan Wang "Outlier detection algorithm based on robust component analysis", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280B (26 July 2018); https://doi.org/10.1117/12.2502094

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