A comparative study on outlier removal from a large-scale dataset using unsupervised anomaly detection

Markus Goldstein, Seiichi Uchida

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Outlier removal from training data is a classical problem in pattern recognition. Nowadays, this problem becomes more important for large-scale datasets by the following two reasons: First, we will have a higher risk of "unexpected" outliers, such as mislabeled training data. Second, a large-scale dataset makes it more difficult to grasp the distribution of outliers. On the other hand, many unsupervised anomaly detection methods have been proposed, which can be also used for outlier removal. In this paper, we present a comparative study of nine different anomaly detection methods in the scenario of outlier removal from a large-scale dataset. For accurate performance observation, we need to use a simple and describable recognition procedure and thus utilize a nearest neighbor-based classifier. As an adequate large-scale dataset, we prepared a handwritten digit dataset comprising of more than 800,000 manually labeled samples. With a data dimensionality of 16×16=256, it is ensured that each digit class has at least 100 times more instances than data dimensionality. The experimental results show that the common understanding that outlier removal improves classification performance on small datasets is not true for high-dimensional large-scale datasets. Additionally, it was found that local anomaly detection algorithms perform better on this data than their global equivalents.

Original languageEnglish
Title of host publicationICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
PublisherSciTePress
Pages263-269
Number of pages7
ISBN (Electronic)9789897581731
Publication statusPublished - 2016
Event5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016 - Rome, Italy
Duration: Feb 24 2016Feb 26 2016

Other

Other5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016
CountryItaly
CityRome
Period2/24/162/26/16

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

Cite this

Goldstein, M., & Uchida, S. (2016). A comparative study on outlier removal from a large-scale dataset using unsupervised anomaly detection. In ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (pp. 263-269). SciTePress.