A research based on application of dimension reduction technology in data visualization using machine learning

Jianwei Chen (), Longlong Bian (), Ajit Kumar () and Rahul Neware ()
Additional contact information
Jianwei Chen: Shijiazhuang Information Engineering Vocational College
Longlong Bian: Shijiazhuang Information Engineering Vocational College
Ajit Kumar: JIIT University
Rahul Neware: Høgskulen På Vestlandet

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 29, 297 pages

Abstract: Abstract At present, the research work of dimension reduction at home and abroad is more about the theoretical exploration and application research of specific dimension reduction methods, and the research on the basic theory of dimension reduction and the spatial properties of high-dimensional data is not perfect. To solve the problem of the application of dimension reduction technology optimization in data visualization, a method of ISOMAP algorithm using Artificial Intelligence is proposed. The specific content of this method uses nonlinear Sammon mapping with short edge first preservation, to improve the "over-clustering" phenomenon of ISOMAP algorithm. When visualizing such data using Artificial Intelligence techniques, furthermore, the application scope of ISOMAP algorithm is extended to a certain extent. The experimental results show that the nonlinear Sammon mapping is sensitive to the initial value and the learning rate, to this end, we use the classical MDs (Molecular dynamics) algorithm to provide a relatively good initial value for the Sammon mapping, we choose three different neighborhood sizes: K = 8, K = 12 and K = 16. K = 8 is appropriate, while K = 12 and K = 16 are inappropriate, this can be verified by the corresponding residual, and the variable substitution method without learning rate is adopted to replace the gradient descent method which is more sensitive to the learning rate. It is proved that the application of nonlinear dimension reduction technology optimization in data visualization has been greatly improved.

Keywords: Machine learning; Dimension reduction; Data visualization; Data mining; Cross validation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-021-01401-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01401-7

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-021-01401-7

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().