A Fast Iteration Method for Mixture Regression Problem

摘要

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.