Advertisement

Principal Component Analysis of Complex Data and Application to Climatology

  • Sergio CamizEmail author
  • Silvia Creta
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

For the study of El Niño phenomenon, winds data collected from the Equator belt of Pacific ocean would be analyzed through PCA. In this paper, the 2-dimensional nature of winds is discussed in respect to the possible ways in which PCA may be implemented. Among others, complex PCA is proposed and compared on a small example to other methods based on real PCA. Then, the first results on a larger data table are illustrated.

Keywords

Principal component analysis Complex principal component analysis El Niño 

References

  1. 1.
    Horel, J.: Complex principal component analysis: theory and examples. J. Clim. Appl. Meteorol. 23, 1660–1673 (1984)CrossRefGoogle Scholar
  2. 2.
    Philander, S.: El Niño, La Niña, and the Southern Oscillation. Academic Press, London (1990)Google Scholar
  3. 3.
    Wang, C., Deser, C., Yu, J.V., Dinezio, P., Clement, A.: El Niño and Southern Oscillation (ENSO): A Review. (2012). https://doi.org/10.1.1.364.4359
  4. 4.
    Camiz, S., Denimal, J., Sosa, W.: Exploratory analysis of Pacific Ocean data to study “El Niño” phenomenon. Revista de la Facultad de Ciencias de la UNI 13(1), 50–58 (2010)Google Scholar
  5. 5.
    Camiz, S., Denimal, J., Purini, R.: New results of multidimensional analysis of TAO/NOAA aata on “El Niño” phenomenon. In: Hucailuk, C., Núñez, N., Molina, E. (eds.) Actas de trabajos completos E-ICES, vol. 9, pp. 24–45. CNEA, Buenos Aires (2014)Google Scholar
  6. 6.
    Jolliffe, I.: Principal Components Analysis. Springer, Berlin (2002)zbMATHGoogle Scholar
  7. 7.
    Preisendorfer, R.W.: In: Mobley, C.D. (eds.) Principal Component Analysis in Meteorology and Oceanography. Elsevier, Amsterdam (1988)Google Scholar
  8. 8.
    Bankó, Z., Dobos, L., Abonyi, J.: Dynamic principal component analysis in multivariate time-series segmentation. Conser. Inf. Evol. Sustain. Eng. Econ. 1(1), 11–24 (2011)Google Scholar
  9. 9.
    Camiz, S., Diblasi, A.: Evolutionary principal component analysis. In: Trabajos Completos, XLI Coloquio Argentino de Estad ística, pp. 680–685. Universidad de Cuyo en Mendoza, Argentina, CD-ROM, 16–18 Octobre 2013Google Scholar
  10. 10.
    Rodrigues, P.C.: Principal component analysis of dependent data. In: 15th European Young Statisticians Meeting September 10–14. Castro Urdiales (Spain) (2007)Google Scholar
  11. 11.
    Autonne, L.: Sur les matrices hypohermitiennes et les unitaires. Comptes rendus des séances hebdomadaires de l’ Académie des sciences de Paris 156, 858–860 (1913)zbMATHGoogle Scholar
  12. 12.
    Stewart, G.: On the early history of the singular value decomposition. SIAM Rev. 35(4), 551–566 (1993)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1, 211–218 (1936)CrossRefGoogle Scholar
  14. 14.
    NOAA: Tropical Atmosphere Ocean Project. Pacific Marine Environmental Laboratory (2015)Google Scholar
  15. 15.
    Haykin, S.: Self-organizing Maps in Neural Networks—A Comprehensive Foundation. Prentice-Hall, Upper Saddle River, NJ (1999)zbMATHGoogle Scholar
  16. 16.
    MATLAB: Release 2012b. The MathWorks, Inc., Natick (MS) (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Dipartimento di MatematicaSapienza Università di RomaRomeItaly
  2. 2.Sapienza Università di RomaRomeItaly

Personalised recommendations