Articles | Volume 27, issue 3
Nonlin. Processes Geophys., 27, 453–471, 2020
https://doi.org/10.5194/npg-27-453-2020
Nonlin. Processes Geophys., 27, 453–471, 2020
https://doi.org/10.5194/npg-27-453-2020

Research article 17 Sep 2020

Research article | 17 Sep 2020

Applications of matrix factorization methods to climate data

Dylan Harries and Terence J. O'Kane

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Cited articles

Aloise, D., Deshpande, A., Hansen, P., and Popat, P.: NP-hardness of Euclidean sum-of-squares clustering, Mach. Learn., 75, 245–248, https://doi.org/10.1007/s10994-009-5103-0, 2009. a
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J. M., and Perona, I.: An extensive comparative study of cluster validity indices, Pattern Recognition, 46, 243–256, 2013. a
Banerjee, A., Merugu, S., Dhillon, I. S., and Ghosh, J.: Clustering with Bregman Divergences, J. Mach. Learn. Res., 6, 1705–1749, https://doi.org/10.1007/s10994-005-5825-6, 2005. a
Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Weather Rev., 115, 1083–1126, https://doi.org/10.1175/1520-0493(1987)115<1083:csapol>2.0.co;2, 1987. a
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., and García-Herrera, R.: The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe, Science, 332, 220–224, https://doi.org/10.1126/science.1201224, 2011. a
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Short summary
Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.