南京师大学报（自然科学版）2017，Vol.40Issue(3)：67-73,7.DOI:10.3969/j.issn.1001-4616.2017.03.010
一种基于逆模拟退火和高斯混合模型的半监督聚类算法
A Semi-supervised Clustering Algorithm Based on Anti-annealing and Gaussian Mixture Model
摘要
Abstract
Semi-supervised Gaussian mixture model(SGMM)based on labeling nodes can improve the accuracy of model parameter estimation. However,the accuracy and convergence of the Expectation Maximization(EM)algorithm are affected by the amount of overlap and mixing coefficients among the Gaussian distributions. In order to improve the accu-racy and speed of the SGMM parameter estimation,the Anti-annealing is combined with the EM algorithm of SGMM. A clustering algorithm of the semi-supervised Gaussian mixture model based on anti-annealing(ASGMM-EM) is proposed. The inverse temperature parameter of the algorithm increases from a smaller value to an upper bound that more than 1 and then back to 1. The semi-supervised clustering EM algorithm is implemented at each inverse temperature parameter. Experiments on synthetic and real data show that the ASGMM-EM is better compared to the algorithms only using semi-supervised or anti-annealing technique.关键词
高斯混合模型/期望最大化算法/逆模拟退火/半监督聚类Key words
Gaussian mixture model/expectation maximization algorithm/anti-annealing/semi-supervised clustering分类
信息技术与安全科学引用本文复制引用
王垚,柴变芳,李文斌,吕峰..一种基于逆模拟退火和高斯混合模型的半监督聚类算法[J].南京师大学报（自然科学版）,2017,40(3):67-73,7.基金项目
国家自然科学基金(61503260)、河北省研究生创新资助项目(CXZZSS2017131). （61503260）
