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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 1
70-74,79

Deep document clustering model via multi-layer subspace semantic fusion

Ren Li'na1,2,3
Qin Yongbin2,3
Huang Ruizhang2,3
Yao Maoxuan1
1. Dept. of Information Engineering, Guizhou Light Industry Technical College, Guiyang 550025, China
2. College of Computer Science & Technology, Guizhou University, Guiyang 550025, China
3. State Key Laboratory of Public Big Data, Guiyang 550025, China
DOI: 10.19734/j.issn.1001-3695.2022.06.0285

Abstract

The traditional deep document clustering methods only use the document semantic representation of the middle layer for clustering, and neglect the problem of the different semantic representation learned by the neural network at different layers and the problem of the feature density of the low-dimensional representation of the middle layer, which is difficult to effectively classify clusters. This paper proposed a deep document clustering model via multi-layer subspace semantic fusion(DCMSF) model. Firstly, the DCMSF model used the autoencoder to extract the latent document semantic representation of different layers. Secondly, this model designed a semantic fusion strategy to map semantic representation of different layers to different subspace to obtain fusion semantics and then clustered them. In addition, this model used the self-representation loss structure of subspace clustering to design a joint loss function to monitor the updating of model parameters. Experimental results show that DCMSF is superior to many mainstream deep document clustering algorithms.

Foundation Support

国家自然科学基金通用联合基金重点资助项目(U1836205)
国家自然科学基金资助项目(62066007,62066008)
贵州轻工职业技术学院院级课题资助项目(21QY07)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0285
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Algorithm Research & Explore
Pages: 70-74,79
Serial Number: 1001-3695(2023)01-011-0070-05

Publish History

[2022-08-17] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

任丽娜, 秦永彬, 黄瑞章, 等. 基于多层子空间语义融合的深度文本聚类 [J]. 计算机应用研究, 2023, 40 (1): 70-74,79. (Ren Li'na, Qin Yongbin, Huang Ruizhang, et al. Deep document clustering model via multi-layer subspace semantic fusion [J]. Application Research of Computers, 2023, 40 (1): 70-74,79. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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