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基于半监督学习的核信任力传播聚类算法

Semi-Supervised Learning Based Kernel Affinity Propagation Clustering Method
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摘要 文中提出一种半监督核信任力传播聚类算法(SSKAPC).SSKAPC在对样本聚类的过程中,引入先验知识提高聚类性能;同时该算法将样本映射到高维空间进行聚类.人工数据和真实世界数据的实验表明,SSKAPC算法能大幅度提高聚类的准确性. In this paper, a semi-supervised kernel affinity propagation method named SSKAPC is proposed. In this method, affinity propagation clustering method is extended to semi-supervised setting, in which background knowledge is provided in terms of pairwise constraints. Kernel trick is also used to process non-linear problem. The experimental results and comparisons on simulated and real-world datasets illustrate the effectiveness and the advantages of the proposed SSKAPC method.
作者 陈晓峰 王士同 曹苏群
机构地区 江南大学信息工程学院 淮阴工学院机械系
出处 《江南大学学报（自然科学版）》 CAS 2008年第5期505-510,共6页 Joural of Jiangnan University (Natural Science Edition)　
基金 国家自然科学基金项目(60773206/F020106) 国防应用基础研究基金项目(A1420461266) 2004年教育部跨世纪优秀人才支持计划基金项目(NCET-04-0496) 2005年教育部科学研究重点基金项目(105087) 2006年江苏省6大人才高峰计划资助项目 中国科学院自动化研究所模式识别国家重点实验室开放课题 南京大学软件新技术国家重点实验室开放课题
关键词 信任力传播 半监督 核聚类 affinity propagation semi-supervised kernel clustering
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献20

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二级参考文献64

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江南大学学报（自然科学版）
2008年 第5期

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