Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

1-2021

Abstract

This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results on several public benchmark datasets show that the proposed method outperforms state-of-the-art unsupervised techniques and is comparable to semi-supervised techniques in most cases.

Keywords

Computer vision, clustering algorithms, benchmark testing, reliability, anomaly detection

Discipline

Computer Sciences | Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

2021 IEEE Winter Conference on Applications of Computer Vision (WACV): Virtual, January 5-9: Proceedings

First Page

3635

Last Page

3644

ISBN

9781665404778

Identifier

10.1109/WACV48630.2021.00368

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/WACV48630.2021.00368

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