Transfer Fuzzy C-Means Clustering Based on Maximum Mean Discrepancy

JIAO Lianmeng; WANG Feng; PAN Quan

doi:10.11999/JEIT220645

Volume 45 Issue 6

Jun. 2023

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JIAO Lianmeng, WANG Feng, PAN Quan. Transfer Fuzzy C-Means Clustering Based on Maximum Mean Discrepancy[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2216-2225. doi: 10.11999/JEIT220645

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JIAO Lianmeng, WANG Feng, PAN Quan. Transfer Fuzzy C-Means Clustering Based on Maximum Mean Discrepancy[J]. Journal of Electronics & Information Technology, 2023, 45(6): 2216-2225. doi: 10.11999/JEIT220645

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Transfer Fuzzy C-Means Clustering Based on Maximum Mean Discrepancy

doi: 10.11999/JEIT220645

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

Funds: The National Natural Science Foundation of China (62171386, 61801386, 61790552), Key Research and Development Program in Shaanxi Province (2022GY-081)

Received Date: 2022-05-19
Rev Recd Date: 2022-12-25

Available Online: 2022-12-28

Publish Date: 2023-06-10

Abstract

Abstract

In this paper, a Transfer Fuzzy C-Means clustering algorithm based on Maximum Mean Discrepancy (TFCM-MMD) is proposed. TFCM-MMD solves the problem that the transfer learning effect of the transfer fuzzy C-means clustering algorithm is weakened when the data distribution between source domain and target domain is very different. The algorithm measures inter-domain differences based on the maximum mean discrepancy criterion, and reduces the differences of data distribution between source domain and target domain in the common subspace by learning the projection matrix of source domain and target domain, so as to improve the effect of transfer learning. Finally, experiments based on synthetic datasets and medical image segmentation datasets verify further the effectiveness of TFCM-MMD algorithm in solving transfer clustering problems with large inter-domain differences.
- Transfer learning,
- Fuzzy clustering,
- Maximum Mean Discrepancy(MMD)

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References(34)

References

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