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TDEC: Evidential Clustering Based on Transfer Learning and Deep Autoencoder

自编码 人工智能 聚类分析 计算机科学 学习迁移 模式识别(心理学) 深度学习 机器学习
作者
Lianmeng Jiao,Feng Wang,Zhunga Liu,Quan Pan
出处
期刊:IEEE Transactions on Fuzzy Systems [Institute of Electrical and Electronics Engineers]
卷期号:32 (10): 5585-5597 被引量:2
标识
DOI:10.1109/tfuzz.2024.3421564
摘要

Evidential clustering is a promising clustering framework using Dempster–Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.
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