Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted either with missing samples or large perturbations. The presence of sparse noise is handled using appropriate use of ℓ 1 -norm along-with use of ℓ2-norm in a convex cost function. For optimization of the cost function, we propose an iteratively reweighted least-squares (IRLS) approach that is suitable for kernel substitution or kernel trick due to availability of a closed form solution. Simulations using real-world temperature data show efficacy of our proposed method, mainly for limited-size training datasets.


Published in:
2019 IEEE InternationalConference on Acoustics, Speech,and Signal Processing. Proceedings, 5426-5430
Presented at:
ICASSP 2019 - IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, 12-17 May, 2019
Year:
2019
Publisher:
IEEE
ISBN:
978-1-4799-8131-1
Laboratories:




 Record created 2019-08-08, last modified 2019-08-12


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