Robust singular value decomposition with application to video surveillance background modelling

The traditional method of computing singular value decomposition (SVD) of a data matrix is based on the least squares principle and is, therefore, very sensitive to the presence of outliers. Hence, the resulting inferences across different applications using the classical SVD are extremely degraded...

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Veröffentlicht in:	Statistics and computing Jg. 34; H. 5
Hauptverfasser:	Roy, Subhrajyoty, Ghosh, Abhik, Basu, Ayanendranath
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2024 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computer Science Decomposition Matrices (mathematics) Modelling Original Paper Probability and Statistics in Computer Science Robustness Singular value decomposition Statistical Theory and Methods Statistics and Computing/Statistics Programs Surveillance Video data Robust matrix factorization Robust SVD Density power divergence Background modelling Singular value decomposition
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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