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A perturbed Gaussian process regression with chunk sparsification for tracking non-stationary systems

Gaussian processes provide a Bayesian regression framework, commonly used in stationary regression by online learning, where the data is fed one by one. In this paper, a perturbed Gaussian process with chunk sparsification is proposed to track non-stationary systems. A chunk sparsification mechanism is designed to select the optimal bases from a chunk of data. The non-stationary system is modeled by a perturbed Gaussian process (PGP), which enables it to track the changing of the system through forgetting the history information at one point or a chunk of points. Finally, PGP is tested on two experiments. The results show that PGP requires less bases than the approximate linear dependence (ALD) sparsification, especially when the data is collected from a trajectory.