Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance

BI Anqi; WANG Shitong

doi:10.11999/JEIT151132

Volume 38 Issue 8

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 2076-2084

BI Anqi, WANG Shitong. Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance[J]. Journal of Electronics & 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 & Information Technology, 2016, 38(8): 2076-2084. doi: 10.11999/JEIT151132

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Transfer Affinity Propagation Clustering Algorithm Based on Kullback-Leiber Distance

doi: 10.11999/JEIT151132

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)

Received Date: 2015-10-10
Rev Recd Date: 2016-04-17
Publish Date: 2016-08-19

Abstract

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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References(32)

References

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