A hybrid anomaly detection method for high dimensional data

Anomaly detection of high-dimensional data is a challenge because the sparsity of the data distribution caused by high dimensionality hardly provides rich information distinguishing anomalous instances from normal instances. To address this, this article proposes an anomaly detection method combinin...

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Veröffentlicht in:	PeerJ. Computer science Jg. 9; S. e1199
Hauptverfasser:	Zhang, Xin, Wei, Pingping, Wang, Qingling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ, Inc 12.01.2023 PeerJ Inc
Schlagworte:	Algorithms and Analysis of Algorithms Anomalies Anomaly detection Autoencoder Classification Data compression Data Mining and Machine Learning Data Science Feature extraction High-dimensional data Least squares Support vector machine Support vector machines High-dimensional data Support vector machine Autoencoder Anomaly detection
ISSN:	2376-5992, 2376-5992
Online-Zugang:	Volltext
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