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Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data

Mirosław Krzyśko et al · Lodz University Press · 2018

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Author / responsible party
Mirosław Krzyśko et al
Publisher
Lodz University Press
Year
2018
ISSN
0208-6018
ISSN
0208-6018
Language
English

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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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APA 7

al, M. K. E. (2018). Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data. https://doi.org/10.18778/0208-6018.337.11

MLA

al, Mirosław Krzyśko et. "Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data." 2018. https://doi.org/10.18778/0208-6018.337.11.

Chicago

al, Mirosław Krzyśko et. 2018. "Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data.". https://doi.org/10.18778/0208-6018.337.11.

Harvard

al, M. K. E. 2018, Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data, Lodz University Press, available at: https://doi.org/10.18778/0208-6018.337.11 [Accessed 7 Aug. 2026].

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Title
Nonlinear Principal Component Analysis for Geographically Weighted Temporal‑spatial Data
Author / contributors
Mirosław Krzyśko et al
Publisher
Lodz University Press
Publication year
2018
ISSN
0208-6018
ISSN
0208-6018
Language
English

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nieliniowa analiza składowych głównych; dane geograficznie ważone; dane czasowo‑przestrzenne
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