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题名Deep generative clustering methods based on disentangled representations and augmented data
作者
发表日期2024-10-01
发表期刊International Journal of Machine Learning and Cybernetics
ISSN/eISSN1868-8071
卷号15期号:10页码:4575-4588
摘要

This paper presents a novel clustering approach that utilizes variational autoencoders (VAEs) with disentangled representations, enhancing the efficiency and effectiveness of clustering. Traditional VAE-based clustering models often conflate generative and clustering information, leading to suboptimal clustering performance. To overcome this, our model distinctly separates latent representations into two modules: one for clustering and another for generation. This separation significantly improves clustering performance. Additionally, we employ augmented data to maximize mutual information between cluster assignment variables and the optimized latent variables. This strategy not only enhances clustering effectiveness but also allows the construction of latent variables that synergistically combine clustering information from original data with generative information from augmented data. Through extensive experiments, our model demonstrates superior clustering performance without the need for pre-training, outperforming existing deep generative clustering models. Moreover, it achieves state-of-the-art clustering accuracy on certain datasets, surpassing models that require pre-training.

关键词Augmented data Clustering Disentangling module Mutual information Variational autoencoder (VAE)
DOI10.1007/s13042-024-02173-9
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收录类别SCIE
语种英语English
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:001209381900001
Scopus入藏号2-s2.0-85191749136
引用统计
文献类型期刊论文
条目标识符https://repository.uic.edu.cn/handle/39GCC9TT/11970
专题理工科技学院
通讯作者Fan, Wentao
作者单位
1.Department of Computer Science and Technology, Huaqiao University, Xiamen, China
2.Guangdong Provincial Key Laboratory IRADS and Department of Computer Science, Beijing Normal University-Hong Kong Baptist University United International College, Zhuhai, China
通讯作者单位北师香港浸会大学
推荐引用方式
GB/T 7714
Xu, Kunxiong,Fan, Wentao,Liu, Xin. Deep generative clustering methods based on disentangled representations and augmented data[J]. International Journal of Machine Learning and Cybernetics, 2024, 15(10): 4575-4588.
APA Xu, Kunxiong, Fan, Wentao, & Liu, Xin. (2024). Deep generative clustering methods based on disentangled representations and augmented data. International Journal of Machine Learning and Cybernetics, 15(10), 4575-4588.
MLA Xu, Kunxiong,et al."Deep generative clustering methods based on disentangled representations and augmented data". International Journal of Machine Learning and Cybernetics 15.10(2024): 4575-4588.
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