Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering

doi:10.1007/s11063-020-10375-9

	Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering
	Zhu, Xiaofei 1; Do, Khoi Duy 2; Guo, Jiafeng 3; Xu, Jun 4; Dietze, Stefan 5
	2020-10-19
发表期刊	NEURAL PROCESSING LETTERS
ISSN	1370-4621
页码	16
摘要	Clustering is an essential data analysis technique and has been studied extensively over the last decades. Previous studies have shown that data representation and data structure information are two critical factors for improving clustering performance, and it forms two important lines of research. The first line of research attempts to learn representative features, especially utilizing the deep neural networks, for handling clustering problems. The second concerns exploiting the geometric structure information within data for clustering. Although both of them have achieved promising performance in lots of clustering tasks, few efforts have been dedicated to combine them in a unified deep clustering framework, which is the research gap we aim to bridge in this work. In this paper, we propose a novel approach, Manifold regularized Deep Embedded Clustering (MDEC), to deal with the aforementioned challenge. It simultaneously models data generating distribution, cluster assignment consistency, as well as geometric structure of data in a unified framework. The proposed method can be optimized by performing mini-batch stochastic gradient descent and back-propagation. We evaluate MDEC on three real-world datasets (USPS, REUTERS-10K, and MNIST), where experimental results demonstrate that our model outperforms baseline models and obtains the state-of-the-art performance.
关键词	Deep neural networks Stacked autoencoder Manifold constraint Clustering
DOI	10.1007/s11063-020-10375-9
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61722211] ; Federal Ministry of Education and Research[01LE1806A] ; Natural Science Foundation of Chongqing[cstc2017jcyjBX0059] ; Beijing Academy of Artificial Intelligence[BAAI2019ZD0306]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000579720300001
出版者	SPRINGER
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15748
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhu, Xiaofei
作者单位	1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China 2.Leibniz Univ Hannover, Res Ctr L3S, D-30167 Hannover, Germany 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China 5.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany
推荐引用方式 GB/T 7714	Zhu, Xiaofei,Do, Khoi Duy,Guo, Jiafeng,et al. Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering[J]. NEURAL PROCESSING LETTERS,2020:16.
APA	Zhu, Xiaofei,Do, Khoi Duy,Guo, Jiafeng,Xu, Jun,&Dietze, Stefan.(2020).Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering.NEURAL PROCESSING LETTERS,16.
MLA	Zhu, Xiaofei,et al."Exploring Implicit and Explicit Geometrical Structure of Data for Deep Embedded Clustering".NEURAL PROCESSING LETTERS (2020):16.