Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 6
1657-1661

Semi-supervised feature extraction method based on LPA-SKFST

Peng Jie1
Gong Xiaofeng1
Li Jian2
1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2. School of Information Engineering, Zhejiang A&F University, Hangzhou 311300, China
DOI: 10.19734/j.issn.1001-3695.2020.09.0244

Abstract

In order to solve the problems of traditional LDA semi supervised feature extraction methods, such as the solution vector is not orthogonal, the solution space is unstable and there is no linear processing ability, this paper proposed LPA-SKFST. In this method, the LPA part increased the proportion of labeled samples through label propagation, semi supervised kernel optimal discriminant vectors SKFST combined KFST, Tikhonov regularization and global preserving regularization by two-way regularization method, and adopted the pair space solution method to ensure the uniform solution form when Fisher's denominator matrix was singular or nonsingular. In the classification experiments of circle, iris, wine and pearl spectral, the accuracy of PCA, LDA, SLDA and SDG groups fluctuated with the change of sample set, labeled sample proportion and label reliability, while LPA-SKFST group kept stable above 85%. The results show that PA-SKFST can overcome the limitations of low proportion of labeled samples and unreliable labeling, and its performance is stable and excellent in both actual set and linear indivisible artificial set.

Foundation Support

浙江省公益技术研究计划资助项目(LGG18F030006)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.09.0244
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 6
Section: Algorithm Research & Explore
Pages: 1657-1661
Serial Number: 1001-3695(2021)06-010-1657-05

Publish History

[2021-06-05] Printed Article

Cite This Article

彭杰, 龚晓峰, 李剑. LPA-SKFST半监督特征提取方法 [J]. 计算机应用研究, 2021, 38 (6): 1657-1661. (Peng Jie, Gong Xiaofeng, Li Jian. Semi-supervised feature extraction method based on LPA-SKFST [J]. Application Research of Computers, 2021, 38 (6): 1657-1661. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)