Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data

Authors

  • Mirosław Krzyśko The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Interfaculty Institute of Mathematics and Statistics
  • Wojciech Łukaszonek The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Interfaculty Institute of Mathematics and Statistics
  • Waldemar Ratajczak Adam Mickiewicz University in Poznań, Faculty of Geographical and Geological Sciences, Institute of Socio‑Economic Geography and Spatial Management
  • Waldemar Wołyński Adam Mickiewicz University in Poznań, Faculty of Mathematics and Computer Science, Department of Probability Theory and Mathematical Statistics

DOI:

https://doi.org/10.18778/0208-6018.337.11

Keywords:

nonlinear principal component analysis, geographically weighted data, temporal‑spatial data

Abstract

Schölkopf, Smola and Müller (1998) have proposed a nonlinear principal component analysis (NPCA) for fixed vector data. In this paper, we propose an extension of the aforementioned analysis to temporal‑spatial data and weighted temporal‑spatial data. To illustrate the proposed theory, data describing the condition of state of higher education in 16 Polish voivodships in the years 2002–2016 are used.

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Published

2018-09-20

How to Cite

Krzyśko, M., Łukaszonek, W., Ratajczak, W., & Wołyński, W. (2018). Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data. Acta Universitatis Lodziensis. Folia Oeconomica, 4(337), 169–181. https://doi.org/10.18778/0208-6018.337.11

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