Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2358-2361

Cascade subspace clustering based on local structure preservation

Xiong Liyan
Zhu Ning
Information Engineering College, East China Jiaotong University, Nanchang 330013, China
DOI: 10.19734/j.issn.1001-3695.2019.02.0062

Abstract

To solve the problem of high-dimensional data clustering, many effective methods, such as the cascade subspace clustering(CSC) had been proposed. However, the clustering loss defined by the CSC may destroy the feature space, which leads to meaningless representative feature subspaces and this in turn hurts clustering performance. To address this issue, this paper proposed an improved algorithm that combines the AutoEncoder to preserve the data structure. Specifically, it used clustering loss as guidance to optimize the feature space and disperse data points. To constrain the manipulation and maintain the local structure between original data and their generating representations, it applied an under-complete AutoEncoder. This paper combined clustering loss and reconstruction loss to optimize the allocation of clustering labels and learn features that are suitable for clustering with local structure preservation. It adopted Adam(adaptive moment estimation) and mini-batch SGD(mini-batch stochastic gradient decent) to optimize parameters for proposed algorithms. On image and text datasets, it used accuracy, normalized mutual information and adjusted rand index to prove the effectiveness and the advantages of the proposed algorithm.

Foundation Support

国家自然科学基金资助项目(61562027)
江西省自然科学基金资助项目(20181BAB202024)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.02.0062
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Algorithm Research & Explore
Pages: 2358-2361
Serial Number: 1001-3695(2020)08-024-2358-04

Publish History

[2020-08-05] Printed Article

Cite This Article

熊李艳, 朱宁. 基于局部结构保留的级联子空间深度聚类 [J]. 计算机应用研究, 2020, 37 (8): 2358-2361. (Xiong Liyan, Zhu Ning. Cascade subspace clustering based on local structure preservation [J]. Application Research of Computers, 2020, 37 (8): 2358-2361. )

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  • 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.

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