基于Kullback-Leiber距离的迁移仿射聚类算法

毕安琪; 王士同

doi:10.11999/JEIT151132

基于Kullback-Leiber距离的迁移仿射聚类算法

doi: 10.11999/JEIT151132

毕安琪¹,
王士同¹

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)

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- 文章访问数: 1199
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- 被引次数: 0
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

Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance

BI Anqi¹,
WANG Shitong¹

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

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。
- 仿射聚类算法 /
- 迁移学习 /
- 人脸数据集 /
- 概率框架 /
- KL距离
Abstract: For solving the clustering problem of transfer learning, a new algorithm called Transfer Affinity Propagation clustering algorithm is proposed based on Kullback-Leiber distance (TAP_KL). Based on the probabilistic framework, a new interpretation of the objective function of Affinity Propagation (AP) clustering algorithm is proposed. By leveraging Kullback-Leiber distance which is usually used in information theory, TAP_KL measures the similarity relationship between source data and target data. Moreover, TAP_KL algorithm can embed the similarity relationship to the calculation of similarity matrix of target data. Thus, the optimization framework of AP can be directly used to optimize the new target function of TAP_KL. In this case, TAP_KL builds a simple algorithm framework to solve the transfer clustering problem, in which the algorithm just needs to modify the similarity matrix to solve the transfer clustering problem. The experimental results based on both 4 datasets show the effectiveness of the proposed algorithm TAP_KL.
- Affinity Propagation(AP) clustering algorithm /
- Transfer learning /
- Face datasets /
- Probabilistic framework /
- Kullback-Leiber distance(KL)

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参考文献(32)

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