Hyperparameter Optimization for Gaussian Process Sequential Regression Models

Abstract

Gaussian Process Sequential Regression Models (GPSRM) is a method to obtain nonlinear cluster structures without requiring the number of clusters. We propose a Markov chain Monte Carlo-based hyperparameter optimization for GPSRM. To evaluate the performance of the proposed method, numerical experiments were conducted on artificial datasets with nonlinear cluster structures. The results show that the proposed method performs better than existing methods at the maximum value of ARI.