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In presence of sparse noise we propose kernel regression for predictingoutput vectors which are smooth over a given graph. Sparse noise models thetraining outputs being corrupted either with missing samples or largeperturbations. 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. Foroptimization of the cost function, we propose an iteratively reweightedleast-squares (IRLS) approach that is suitable for kernel substitution orkernel trick due to availability of a closed form solution. Simulations usingreal-world temperature data show efficacy of our proposed method, mainly forlimited-size training datasets.

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise