A Study on Parameter Estimation in Gaussian Process based c-Regression Models

Abstract

Gaussian process based c-regression models (GPCRM) is a method to obtain the cluster partition and nonlinear regression models simultaneously. Since the regression model depends on the kernel parameters, it is difficult to obtain a regression model that captures the data structure depending on the kernel parameters. In this paper, we propose maximum marginal likelihood GPCRM(MML-GPCRM) as a method introducing kernel parameter estimation. The experimental results suggest that the MML-GPCRM estimated a better-fitting regression models than the GPCRM.