Original title: Feature Space Reduction As Data Preprocessing For The Anomaly Detection
Authors: Bilik, Simon
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: In this paper, we present two pipelines in order to reduce the feature space for anomalydetection using the One Class SVM. As a first stage of both pipelines, we compare the performanceof three convolutional autoencoders. We use the PCA method together with t-SNE as the first pipelineand the reconstruction errors based method as the second. Both methods have potential for theanomaly detection, but the reconstruction error metrics prove to be more robust for this task. Weshow that the convolutional autoencoder architecture doesn’t have a significant effect for this task andwe prove the potential of our approach on the real world dataset.
Keywords: Anomaly detection; CNN; Convolutional autoencoder; OC-SVM; PCA; t-SNE
Host item entry: Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers, ISBN 978-80-214-5942-7

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200792

Permalink: http://www.nusl.cz/ntk/nusl-447837


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