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Technology of Network & Communication
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 8
2415-2420

Principal component analysis of histogram data with non-negative coefficients based on quantile function

Li Zhuting
Chen Xiuhong
Sun Huiqiang
School of Digital Media, Jiangnan University, Wuxi Jiangsu 214122, China
DOI: 10.19734/j.issn.1001-3695.2018.03.0151

Abstract

Since the existing PCA of symbolic data mostly use some representative information instead of symbolic data, this paper proposed a histogram principal component analysis. It represented a histogram data by a quantile function with its cha-racteristic, and introduced the Wasserstein distance which fully took into account the probability distribution of the histogram data. It was easy to obtain the covariance matrix to perform the principal component analysis using this distance. However, the eigenvectors corresponding to the first m largest eigenvalues obtained by this method was not necessarily negative, so it could not guarantee that the principal components were also quantile functions when they were represented by the quantile functions. For this point, it combined the idea of DSD regression model, defined the corresponding symmetric distribution variables for each histogram variable, then obtained the non-negative principal component coefficients with the generalized covariance matrix. The experiments show the effectiveness of the algorithm. Besides, this method overcomes the disadvantage that the PCA coefficient of the histogram may be negative and retains more information of the original data.

Foundation Support

国家自然科学基金资助项目(61373055)
2017年江苏省研究生科研创新计划资助项目(KYCX17_1500)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0151
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 8
Section: Technology of Network & Communication
Pages: 2415-2420
Serial Number: 1001-3695(2019)08-036-2415-06

Publish History

[2019-08-05] Printed Article

Cite This Article

李竹婷, 陈秀宏, 孙慧强. 基于分位函数的直方图符号数据非负主成分分析法 [J]. 计算机应用研究, 2019, 36 (8): 2415-2420. (Li Zhuting, Chen Xiuhong, Sun Huiqiang. Principal component analysis of histogram data with non-negative coefficients based on quantile function [J]. Application Research of Computers, 2019, 36 (8): 2415-2420. )

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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