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Authors: Fumito Ebuchi ; Aiga Suzuki and Masahiro Murakawa

Affiliation: Graduate School of Systems and Information Engineering, University of Tsukuba, Japan National Institute of Advanced Industrial Science and Technology (AIST), Japan

Keyword(s): Subspace Method, Anomaly Detection, Optimization Problems.

Abstract: In conventional anomaly detection methods, the classifier is usually trained only with normal data. However, real-world problems may present a very small amount of anomalous data. In this paper, we propose an improved subspace method for anomaly detection that has the ability to utilize a very small amount of anomalous data. Our method introduces an objective function that minimizes the average projection length of anomalous data into the conventional objective function for the subspace method. This formulation enables a normal subspace that considers the distribution of anomalous data to be learned, thereby improving the anomaly detection performance. Furthermore, because the information about anomalous data is provided in the form of the average projection length, stable detection can be expected even when an extremely small amount of anomalous data is used. We used MNIST and the CIFAR-10 dataset to evaluate the effectiveness of the proposed method, which yielded a higher anomaly d etection performance compared with the conventional normal model or classifier model under conditions in which very little anomalous data are obtainable. The performance of our method on CIFAR-10 was assessed by imposing the constraint that only four or five anomalous data samples could be used. In this test, our method achieved an average AUC of 0.263 points higher than that of the state-of-the-art method using only normal data. (More)

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Paper citation in several formats:
Ebuchi, F.; Suzuki, A. and Murakawa, M. (2020). Improved Subspace Method for Supervised Anomaly Detection with Minimal Anomalous Data. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-758-397-1; ISSN 2184-4313, pages 151-158. DOI: 10.5220/0008918401510158

@conference{icpram20,
author={Fumito Ebuchi. and Aiga Suzuki. and Masahiro Murakawa.},
title={Improved Subspace Method for Supervised Anomaly Detection with Minimal Anomalous Data},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM,},
year={2020},
pages={151-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008918401510158},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Improved Subspace Method for Supervised Anomaly Detection with Minimal Anomalous Data
SN - 978-989-758-397-1
IS - 2184-4313
AU - Ebuchi, F.
AU - Suzuki, A.
AU - Murakawa, M.
PY - 2020
SP - 151
EP - 158
DO - 10.5220/0008918401510158