Online local Gaussian process for tensor-variate regression: Application to fast reconstruction of limb movements from brain signal

Tensor-variate regression approaches have been spotlighted over the past years, due to the fact that many challenging regression tasks in the real world involve in high-order tensorial data. However, these approaches are often computationally prohibitive, which limits the predictive performance for large data sets. In this paper, we propose a computationally-efficient tensor-variate regression approach in which the latent function is flexibly modeled by using online local Gaussian process (OLGP). By doing so, the large data set is efficiently processed by constructing a number of small-sized GP experts in an online fashion. Furthermore, we introduce two efficient search strategies to find local GP experts to make accurate predictions with a Gaussian mixture representation. Finally, we evaluate our approach on a real-life regression task, reconstruction of limb movements from brain signal, to show its effectiveness and scalability for large data sets.