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| Cross-Entropy Semi-supervised Clustering Based on Pairwise Constraints |
| LI Chaoming1, XU Shengbing1,2, HAO Zhifeng1,3 |
1.School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520
2.School of Computers, Guangdong University of Technology, Guangzhou 510006
3.School of Mathematics and Big Data, Foshan University, Foshan 528000 |
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Abstract The objective function used in the classical maximum entropy clustering(MEC) lacks the information expression on pairwise constraints. Therefore, the effective supervision information is wasted when a small amount of pairwise constraints are known. In this paper, an algorithm of cross-entropy semi-supervised clustering(CE-sSC) based on pairwise constrains on the basis of MEC algorithm is proposed. The sample cross-entropy is utilized to describe the pairwise constraints information and introduced to the objective function of MEC as a penalty term. With Lagrange optimization procedure, the objective function can be resolved into the cluster center and the membership update equations. Experimental results indicate the proposed method effectively improves the clustering performance by using a small amount of pairwise constraints and works well on actual datasets.
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Received: 14 November 2016
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| Fund:Supported by Science and Technology Planning Project of Guangdong Province(No.2015A070704049), Youth Fund of Guangdong University of Technology(No.405085084), Undergraduate Experimental Teaching Reform and Research Project of Guangdong University of Technology(No.262523346) |
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Corresponding Authors:
(XU Shengbing(Corresponding author), born in 1974, master, lecturer. His research interests include mathematical modeling and transfer learning.)
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About author:: (LI Chaoming, born in 1991, master student. His research interests include pa-ttern recognition and machine learning.)
(XU Shengbing(Corresponding author), born in 1974, master, lecturer. His research interests include mathematical modeling and transfer learning.)
(HAO Zhifeng, born in 1968, Ph.D., professor. His research interests include machine learning and artificial intelligence.) |
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2017, Vol. 30 
