| 基于自编码器的深度聚类算法综述 |
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| 引用本文： | 陶文彬,钱育蓉,张伊扬,马恒志,冷洪勇,马梦楠.基于自编码器的深度聚类算法综述[J].计算机工程与应用,2022,58(18):16-25. |
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| 作者姓名： | 陶文彬 钱育蓉 张伊扬 马恒志 冷洪勇 马梦楠 |
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| 作者单位： | 1.新疆大学 软件学院，乌鲁木齐 830046
2.新疆大学 软件学院 重点实验室，乌鲁木齐 830046
3.新疆维吾尔自治区 信号检测与处理重点实验室，乌鲁木齐 830046
4.北京理工大学 计算机学院，北京 100081 |
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| 摘 要： | 聚类分析作为一种常见的分析方法，广泛应用于各种场景。随着机器学习技术的发展，深度聚类算法也成了当下研究的热点，基于自编码器的深度聚类算法是其中的代表算法。为了及时了解掌握基于自编码器的深度聚类算法的发展，介绍了四种自编码器的模型，对近些年代表性的算法依照自编码器的结构进行了分类。在MNIST、USPS、Fashion-MNIST数据集上，针对传统聚类算法和基于自编码器的深度聚类算法进行了实验对比、分析，最后对基于自编码器的深度聚类算法目前存在的问题进行了总结，展望了深度聚类算法的研究方向。
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| 关 键 词： | 聚类算法 深度聚类 自编码器 特征提取 |
Survey of Deep Clustering Algorithm Based on Autoencoder |
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| TAO Wenbin,QIAN Yurong,ZHANG Yiyang,MA Hengzhi,LENG Hongyong,MA Mengnan.Survey of Deep Clustering Algorithm Based on Autoencoder[J].Computer Engineering and Applications,2022,58(18):16-25. |
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| Authors: | TAO Wenbin QIAN Yurong ZHANG Yiyang MA Hengzhi LENG Hongyong MA Mengnan |
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| Affiliation: | 1.College of Software, Xinjiang University, Urumqi 830046, China
2.Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830046, China
3.Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi 830046, China
4.School of Computer Science, Beijing University of Technology, Beijing 100081, China |
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| Abstract: | As a common analysis method, cluster analysis is widely used in various scenarios. With the development of machine learning technology, deep clustering algorithm has also become a hot research topic, and the deep clustering algorithm based on autoencoder is one of the representative algorithms. To keep abreast of the development of deep clustering algorithms based on autoencoders, four models of autoencoders are introduced, and the representative algorithms in recent years are classified according to the structure of autoencoders. For the traditional clustering algorithm and the deep clustering algorithm based on autoencoder, experiments are compared and analyzed on the MNIST, USPS, Fashion-MNIST datasets. At last, the current problems of deep clustering algorithms based on autoencoders are summarized, and the possible research directions of deep clustering algorithms are prospected. |
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| Keywords: | clustering algorithm deep clustering autoencoder feature extraction |
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