Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Arun Venkitaraman; Pascal Frossard; Saikat Chatterjee

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arXiv · 1811.02314

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

Arun Venkitaraman·Pascal Frossard·Saikat Chatterjee

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Abstract

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 $\ell_1$-norm along-with use of $\ell_2$-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.

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BibTeX RIS

Arun Venkitaraman, Pascal Frossard, Saikat Chatterjee. 2018-11-06. Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise. https://arxiv.org/abs/1811.02314

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