An efficient EM algorithm for two-layer mixture model of gaussian process functional regressions

Abstract

The mixture of Gaussian processes is effective for regression, but it cannot handle the non-stationary curve clustering problem well. The two-layer mixture of Gaussian process functional regressions (TMGPFR) model was established to deal with this problem. In this paper, we first propose the classification EM (CEM) algorithm to solve that the optimization algorithm is inefficient for TMGPFRs, and then propose the deterministic annealing CEM algorithm for TMGPFRs to overcome the local maximum problem of the CEM algorithm. Lastly, experiments are conducted on synthetic and real-world data sets, and the results show that our proposed algorithms are more effective than the compared algorithms on curve clustering and regression.