In this paper, we propose a Fast Iteration Method for solving mixture regression problem, which can be treated as a model-based clustering. Compared to the EM algorithm, the proposed method is faster, more flexible and can solve mixture regression problem with different error distributions (i.e. Laplace and t distribution). Extensive numeric experiments show that our proposed method has better performance on randomly simulations and real data.
Ingrassia, S., Minotti, S.C. and Punzoa, A. (2014) Model-Based Clustering via Linear Cluster-Weighted Models. Computational Statistics and Data Analysis, 71, 159-182. http://dx.doi.org/10.1016/j.csda.2013.02.012
Song, W.X., Yao, W.X. and Xing, Y.R. (2014) Robust Mixture Regression Model Fitting by Laplace Distribution. Computational Statistics and Data Analysis, 71, 128-137. http://dx.doi.org/10.1016/j.csda.2013.06.022