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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Detalles Bibliográficos

Autor principal:	Taha J. Alhindi
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2025-12-01
Colección:	Mathematics
Materias:	deep support vector data description (Deep SVDD) graph-based manifold regularization multi-channel time-series anomaly detection fire monitoring semi-supervised anomaly detection
Acceso en línea:	https://www.mdpi.com/2227-7390/13/24/3987
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