Anomaly detection for blueberry data using sparse autoencoder-support vector machine

High-dimensional space includes many subspaces so that anomalies can be hidden in any of them, which leads to obvious difficulties in abnormality detection. Currently, most existing anomaly detection methods tend to measure distances between data points. Unfortunately, the distance between data poin...

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Veröffentlicht in:	PeerJ. Computer science Jg. 9; S. e1214
Hauptverfasser:	Wei, Dianwen, Zheng, Jian, Qu, Hongchun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 10.03.2023 PeerJ, Inc PeerJ Inc
Schlagworte:	Accuracy Anomalies Anomaly detection Artificial Intelligence Auto encoder Chebyshev approximation Data mining Data Mining and Machine Learning Data points Datasets High dimensionality Kernel functions Machine learning Methods Neural Networks Outliers (Statistics) Standard deviation Subspaces Support vector machine Support vector machines Theorems China Auto encoder High dimensionality Support vector machine Anomaly detection
ISSN:	2376-5992, 2376-5992
Online-Zugang:	Volltext
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