|Table of Contents|

A Semi-supervised Clustering Algorithm Based onAnti-annealing and Gaussian Mixture Model(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2017年03期
Page:
67-
Research Field:
·计算机科学·
Publishing date:

Info

Title:
A Semi-supervised Clustering Algorithm Based onAnti-annealing and Gaussian Mixture Model
Author(s):
Wang Yao; Chai Bianfang; Li Wenbin; Lü Feng
School of Information Engineering,Hebei GEO University,Shijiazhuang 050031,China
Keywords:
Gaussian mixture model; expectation maximization algorithm; anti-annealing; semi-supervised clustering
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2017.03.010
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 accuracy 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.

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Last Update: 2017-09-30