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From tensors to novelties: Low-dimensional representations for anomaly detection in multispectral imagery

Anomaly detection in multispectral imagery must cope with high-dimensional inputs, scarce labeled anomalies and operational constraints. Tensor decompositions offer a structured way to compress such data, but their impact on anomaly detection performance and cost is not well quantified. This work st...

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Hoofdauteur: Anthony Chan Chan
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: Elsevier 2026-03-01
Reeks:Machine Learning with Applications
Onderwerpen:
Tensor decomposition
CP
Tucker
Anomaly detection
Multispectral data
Online toegang:http://www.sciencedirect.com/science/article/pii/S266682702600023X
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