Abstract
This paper addresses the problem of recovering the graph signal over sensor networks with abnormal observations. The conventional adaptive graph recovery approach, namely, the graph least mean square (LMS) algorithm, cannot reconstruct the graph signal corrupted by non-Gaussian noise. A normalized least mean M-estimator algorithm is devised to balance efficiency and robustness. Our method combines the advantages of the graph normalized LMS and graph normalized sign algorithms, possessing the former's convergence speed and the latter's robustness. Stochastic analysis of the proposed updating rule is provided, while two time-varying algorithmic improvements are suggested. Finally, computer simulation results demonstrate the performance superiority of the developed approach over state-of-the-art schemes. © 2026 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 326-340 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Signal and Information Processing over Networks |
| Volume | 12 |
| Online published | 27 Feb 2026 |
| DOIs | |
| Publication status | Published - 2026 |
Funding
This work was supported in part by the National Natural Science Foundation of China under Grant 62506315, and in part by the City University of Hong Kong under Grant 9610639.
Research Keywords
- Adaptive signal recovery
- graph signal processing
- M-estimator
- normalized least mean square algorithm
- robust signal recovery
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