Applying the heavy-tailed kernel to the gaussian process regression for modeling point of sale data

Abstract

Heavy-tailed distributions such as student’s t distribution have a special position in the statistical machine learning research due to their robustness when handling Gaussian noise model or other models within unknown types of noise. In this paper, we focus on using the robust kernel as an alternative to the wildly used squared exponential kernel for promoting the model’s robustness. Furthermore, we apply the heavy-tailed kernel to the Gaussian process with Bayesian regression for predicting the daily turnover of merchandises based on learning Point of Sale (PoS) data. The experiment results show better and more robust performs when comparing with other kernels.