Fast Approximation Method for Gaussian Process Regression Using Hash Function for Non-uniformly Distributed Data

Yuya Okadome, Yutaka Nakamura, Yumi Shikauchi, Shin Ishii, Hiroshi Ishiguro

Abstract: Gaussian process regression (GPR) has the ability to deal with non-linear regression readily, although the calculation cost increases with the sample size. In this paper, we propose a fast approximation method for GPR using both locality-sensitive hashing and product of experts models. To investigate the performance of our method, we apply it to regression problems, i.e., artificial data and actual hand motion data. Results indicate that our method can perform accurate calculation and fast approximation of GPR even if the dataset is non-uniformly distributed.