Clusterwise Multivariate Kernel Ridge Regression Analysis with an Application

Abstract

This paper considers a situation where multiple explanatory variables and multiple objective variables are observed from several populations. When observed data with multiple explanatory variables together with a single objective variable is available from the uncertain number of populations, a clusterwise regression analysis has been proposed to obtain an efficient regression model. In this paper, we propose a clusterwise multivariate kernel ridge regression analysis to analyze nonlinear data including both multiple explanatory variables and multiple objective variables from several populations. In addition, through a numerical experiment and a case study, we show that the performance of the clusterwise multivariate kernel ridge regression analysis is superior to those of traditional k-means cluster analysis and kernel k-means cluster analysis.