Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 8
2354-2358

Feature extraction for functional data by combining phase and amplitude information

Jin Haibo1
Ma Haiqiang2
1. Dept. of Mathematic, Taiyuan University of Science & Technology, Taiyuan 030024, China
2. School of Statistics, Jiangxi University of Finance & Economics, Nanchang 330013, China
DOI: 10.19734/j.issn.1001-3695.2020.10.0363

Abstract

The current functional data analysis focuses on the amplitude variability, while ignoring the phase variability. In many cases, phase variability is a kind of useful information for statistical analysis. Based on partial least square method, this paper proposed a feature extraction procedure for functional data by combining phase and amplitude information. Firstly, the proposed procedure used a function alignment technique to obtain the time warping function containing phase variation. Secondly, it combined the aligned function and the warping function in the form of piecewise function. Finally, it used the partial least square approach to extract component features from the combined function and utilized those features on the regression and classification model. Experimental results show that the proposed method can obtain better prediction performance than the principal component analysis method.

Foundation Support

中国博士后科学基金面上资助项目(2019M662262)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.10.0363
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 8
Section: Algorithm Research & Explore
Pages: 2354-2358
Serial Number: 1001-3695(2021)08-019-2354-05

Publish History

[2021-08-05] Printed Article

Cite This Article

金海波, 马海强. 相幅组合的函数型数据特征提取方法研究 [J]. 计算机应用研究, 2021, 38 (8): 2354-2358. (Jin Haibo, Ma Haiqiang. Feature extraction for functional data by combining phase and amplitude information [J]. Application Research of Computers, 2021, 38 (8): 2354-2358. )

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)