Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2613-2617

Semi-supervised auto-encoder using sparse and label regularizations for classification

Wang Huiling1
Song Wei1,2
Wang Chenni1,2
1. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
2. Engineering Research Center of Internet of Things Technology Applications for Ministry of Education, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2018.02.0147

Abstract

Auto-encoder could express the semantic features of data through deep unsupervised learning, but it was hard to determine the nodes of hidden layer and the processing of data for classification often leads to low accuracy and low stability. To solve the problems, this paper proposed a semi-supervised auto-encoder using sparse and label regularizations(LSRAE) to extract the essential features of the samples more accurately by combining unsupervised learning with supervised learning. The sparse regularization term added constraints to the response of each hidden node, so that this algorithm could find potential structures in the data when the number of hidden neurons was large. At the same time, this algorithm introduced a label regularization term to compare the actual labels with desired labels by supervised learning to adjust the network parameters and further improve the classification accuracy. In order to verify the validity of the proposed method, this algorithm tested many data sets in the experiment. The results show that compared with traditional auto-encoders(AE), sparse auto-encoder(SAE), and extreme learning machine(ELM), SLRAE can obviously improve the classification accuracy and stability when the processed data is applied to the same classifier.

Foundation Support

国家自然科学基金资助项目(61673193)
中央高校本科研业务费专项资金资助项目(JUSRP51635B,JUSRP51510)
江苏省自然科学基金资助项目(BK20150159)
中国博士后科学基金资助项目(2017M621625)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.02.0147
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: Algorithm Research & Explore
Pages: 2613-2617
Serial Number: 1001-3695(2019)09-012-2613-05

Publish History

[2019-09-05] Printed Article

Cite This Article

王慧玲, 宋威, 王晨妮. 稀疏和标签约束半监督自动编码器的分类算法 [J]. 计算机应用研究, 2019, 36 (9): 2613-2617. (Wang Huiling, Song Wei, Wang Chenni. Semi-supervised auto-encoder using sparse and label regularizations for classification [J]. Application Research of Computers, 2019, 36 (9): 2613-2617. )

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.

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