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基于Kullback-Leiber距离的迁移仿射聚类算法

毕安琪 王士同

毕安琪, 王士同. 基于Kullback-Leiber距离的迁移仿射聚类算法[J]. 电子与信息学报, 2016, 38(8): 2076-2084. doi: 10.11999/JEIT151132
引用本文: 毕安琪, 王士同. 基于Kullback-Leiber距离的迁移仿射聚类算法[J]. 电子与信息学报, 2016, 38(8): 2076-2084. doi: 10.11999/JEIT151132
BI Anqi, WANG Shitong. Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance[J]. Journal of Electronics and Information Technology, 2016, 38(8): 2076-2084. doi: 10.11999/JEIT151132
Citation: BI Anqi, WANG Shitong. Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance[J]. Journal of Electronics and Information Technology, 2016, 38(8): 2076-2084. doi: 10.11999/JEIT151132

基于Kullback-Leiber距离的迁移仿射聚类算法

doi: 10.11999/JEIT151132
基金项目: 

国家自然科学基金(61170122, 61272210),江苏省 2014 年度普通高校研究生科研创新计划项目(KYLX_1124),山东省高等学校科技计划项目(J14LN05)

Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance

Funds: 

The National Natural Science Foundation of China (61170122, 71272210), Jiangsu Graduate Student Innovation Projects (KYLX_1124), The Science and Technology Program Shandong Provinceial Higher Education (J14LN05)

  • 摘要: 针对迁移聚类问题,该文提出一种新的基于Kullback-Leiber距离的迁移仿射聚类算法(TAP_KL)。该算法从概率角度重新解释AP算法的目标函数,并借助于信息论中最常见的一种距离度量,即Kullback-Leiber距离,测量源域与目标域代表点的相似性。另外,通过详细分析TAP_KL算法与AP算法的目标函数,得出一个重要结论,即可以将源域与目标域的相似性嵌入到目标域数据集相似性矩阵的计算中,从而直接利用AP算法的优化算法优化TAP_KL算法的目标函数,解决基于代表点的迁移聚类问题。最后,通过基于4个数据集的仿真实验,进一步验证了TAP_KL算法在解决迁移聚类问题时的有效性。
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    出版历程
    • 收稿日期:  2015-10-10
    • 修回日期:  2016-04-17
    • 刊出日期:  2016-08-19

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