Low-complexity Graph Sampling With Noise and Signal Reconstruction via Neumann Series

Graph sampling addresses the problem of selecting a node subset in a graph to collect samples, so that a K-bandlimited signal can be reconstructed with high fidelity. Assuming an independent and identically distributed (i.i.d.) noise model, minimizing the expected mean square error (MMSE) leads to t...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 67; H. 21; S. 5511 - 5526
Hauptverfasser:	Wang, Fen, Cheung, Gene, Wang, Yongchao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Approximation Complexity theory Covariance matrices Fourier transforms Graph signal processing Inversions Laplace equations Matrix decomposition matrix inversion Neumann series theorem Noise Optimality criteria Optimization Robustness (mathematics) Sampling Sampling methods Signal reconstruction Strategy
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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