The Application of the Accelerated Proximal Gradient Descent Algorithm for the Solution of the Weighted Schatten-p Norm in Sparse Noise Extraction

In this study, we introduce the weighted Schatten-p norm optimization robust principal component analysis model, transforming the nuclear norm into a combination of the Schatten-p norm and the weighted nuclear norm for enhanced flexibility. We adapt the model for compatibility with the Accelerated P...

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Veröffentlicht in:	Circuits, systems, and signal processing Jg. 43; H. 8; S. 5169 - 5190
Hauptverfasser:	Wang, Jiajun, Chen, Jing, Zhu, Quanmin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.08.2024 Springer Nature B.V
Schlagworte:	Algorithms Approximation Circuits and Systems Convex analysis Dimensional analysis Electrical Engineering Electronics and Microelectronics Engineering Image enhancement Instrumentation Lagrange multiplier Machine learning Methods Noise Optimization Parameter identification Principal components analysis Signal processing Signal,Image and Speech Processing RPCA Accelerated proximal gradient descent algorithm Weighted nuclear norm Schatten-p norm Sparse noise Low-rank matrix recovery
ISSN:	0278-081X, 1531-5878
Online-Zugang:	Volltext
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