Anomaly-Aware Graph-Based Semi-Supervised Deep Support Vector Data Description for Anomaly Detection

Anomaly detection in safety-critical systems often operates under severe label constraints, where only a small subset of normal and anomalous samples can be reliably annotated, while large unlabeled data streams are contaminated and high-dimensional. Deep one-class methods, such as deep support vect...

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Hlavní autor:	Taha J. Alhindi
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-12-01
Edice:	Mathematics
Témata:	deep support vector data description (Deep SVDD) graph-based manifold regularization multi-channel time-series anomaly detection fire monitoring semi-supervised anomaly detection
On-line přístup:	https://www.mdpi.com/2227-7390/13/24/3987
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