loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

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)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.147.75.46

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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, SciTePress, 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
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic