Gaussian Process Based Sequential Regression Models

The c-regression model is a method that simultaneously performs clustering and regression to obtain regression equations for each cluster and express the overall structure of the dataset. Gaussian Process c-Regression Models has been proposed as a method to extend the c-regression model to nonlinear models. In this paper, we propose Gaussian Process Sequential Regression Models, which do not require the number of clusters and can obtain nonlinear regression models. The optimization of kernel parameters used in the proposed method using the gradient method, or MCMC was also studied. The experimental results suggested that the proposed method outperforms the conventional clustering methods in terms of the maximum and average values of ARI.