Efficient and Fast Joint Sparse Constrained Canonical Correlation Analysis for Fault Detection

The canonical correlation analysis (CCA) has attracted wide attention in fault detection (FD). To improve the detection performance, we propose a new joint sparse constrained CCA (JSCCCA) model that integrates the <inline-formula> <tex-math notation="LaTeX">\ell_{2,0}</tex-m...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 35; H. 3; S. 1 - 11
Hauptverfasser:	Xiu, Xianchao, Pan, Lili, Yang, Ying, Liu, Wanquan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	0}</tex-math> </inline-formula>-norm joint sparse <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> ell_{2 Algorithms canonical correlation analysis (CCA) Computing time Constraints Convergence Correlation Correlation analysis Fault detection fault detection (FD) Iterative methods Manifolds Minimization Optimization optimization algorithm Sparse matrices
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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