DSP-TMM:A Robust Cluster Analysis Method Based on Diversity Self-Paced T-Mixture Model

作     者：Limin Pan Xiaonan Qin Senlin Luo Limin Pan;Xiaonan Qin;Senlin Luo

作者机构：Information System and Security&Countermeasures Experimental CenterBeijing Institute of TechnologyBeijing 100081China 

出 版 物：《Journal of Beijing Institute of Technology》 (北京理工大学学报（英文版）)

年 卷 期：2020年第29卷第4期

页      面：531-543页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the 13th 5-Year National Science and Technology Supporting Project(2018YFC2000302) 

主　　题：cluster analysis Gaussian mixture model t-distribution mixture model self-paced learning initialization 

摘      要：In order to implement the robust cluster analysis,solve the problem that the outliers in the data will have a serious disturbance to the probability density parameter estimation,and therefore affect the accuracy of clustering,a robust cluster analysis method is proposed which is based on the diversity self-paced t-mixture *** model firstly adopts the t-distribution as the submodel which tail is easily *** this basis,it utilizes the entropy penalty expectation conditional maximal algorithm as a pre-clustering step to estimate the initial *** that,this model introduces l2,1-norm as a self-paced regularization term and developes a new ECM optimization algorithm,in order to select high confidence samples from each component in ***,experimental results on several real-world datasets in different noise environments show that the diversity self-paced t-mixture model outperforms the state-of-the-art clustering *** provides significant guidance for the construction of the robust mixture distribution model.