文献类型：期刊文献

英文题名：Merging Student's-t and Rayleigh distributions regression mixture model for clustering time-series

作者：Zhu, Hongqing[1];Pan, Xu[1];Xie, Qunyi[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2017

卷号：266

起止页码：247

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20172203718434);WOS:【SCI-EXPANDED(收录号:WOS:000408183900024)】；

基金：The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments that significantly improved the quality of this paper, This work was supported by the National Natural Science Foundation of China under Grant 61371150.

语种：英文

外文关键词：Student's-t distribution; Rayleigh distribution; Time-series; Mean template; Markov random field; Clustering

摘要：This paper addresses an efficient scheme for clustering time-series through a novel regression mixture strategy (RMM) that simultaneously utilizes the benefits of the Markov random field (MRF) and mean template. Each component of the proposed RMM is a mixture of Student's-t and non-symmetric Rayleigh distributions. The Student's-t distribution has been validated to be effective in reducing the influence of outliers. Another advantage is it considers the property of time-series in which spatially adjacent voxels have higher probabilities when belonging to the same cluster; thus, this study seeks to impose the spatially interdependent constraints in the form of a prior through a Bayesian theorem. By introducing the non-symmetric Rayleigh distribution, the proposed method has flexibility to fit various types of observed time-series. To optimize the model parameters, the proposed algorithm is formulated as a maximum a posteriori (MAP) estimation problem. An expectation maximization (EM)-type approach is eventually executed to obtain the update equations. Additionally, an efficient energy function is applied in the proposed approach such that the EM-MAP algorithm can be directly applied to calculate the objective function, which makes the proposed approach easier to implement. To illustrate the suitability of the proposed approach, we have extensively compared clustering accuracy, curve square error, and intraclass index with those of existing similarity methods on synthetic spatiotemporal datasets, synthetic fMRI time-series, and real life fMRI time-series. (C) 2017 Elsevier B.V. All rights reserved.