Skip to main content
SpringerLink
Log in

Diffusion Maps for the Description of Meteorological Data

  • Conference paper
Book cover Hybrid Artificial Intelligent Systems (HAIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7208))

Included in the following conference series:

  • 1782 Accesses

Abstract

Diffusion Maps is a new powerful technique for dimensionality reduction that can capture geometric structure while taking into account data distribution. In this work we will apply it to time and spatial compression of numerical weather forecasts, showing how it is capable to greatly reduce the initial dimension while still capturing relevant information in the original data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Belkin, M., Nyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15, 1373–1396 (2003)

    Article  MATH  Google Scholar 

  2. Bengio, Y., Delalleau, O., Le Roux, N., Paiement, J.F., Vincent, P., Ouimet, M.: Learning eigenfunctions links spectral embedding and kernel PCA. Neural Computation 16, 2197–2219 (2004)

    Article  MATH  Google Scholar 

  3. Biernacki, C., Celeux, G., Govaert, G.: Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 719–725 (2000)

    Article  Google Scholar 

  4. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  5. Coifman, R., Lafon, S.: Diffusion Maps. Applied and Computational Harmonic Analysis 21, 5–30 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  6. Coifman, R., Lafon, S., Lee, A., Maggioni, N., Nadler, B., Warner, F., Zucker, S.: Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps. Proceedings of the National Academy of Sciences 102, 7432–7437 (2005)

    Article  Google Scholar 

  7. European Center for Medium–Range Weather Forecasts: http://www.ecmwf.int/

  8. Hammerly, G., Elkan, C.: Learning the K in K–means. In: Advances In Neural Information Processing Systems, vol. 17 (2003)

    Google Scholar 

  9. Nadler, B., Lafon, S., Kevrekidis, I.G., Coifman, R.: Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck operators. In: Advances In Neural Information Processing Systems, vol. 18 (2005)

    Google Scholar 

  10. Rabin, N.: Data mining in dynamically evolving systems via diffusion methodologies. PhD Thesis, Tel-Aviv University (2010)

    Google Scholar 

  11. Rogers, L.C.G., Williams, D.: Diffusions, Markov processes, and martingales. Foundations, vol. 1. Cambridge Mathematical Library (2000)

    Google Scholar 

  12. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–895 (2000)

    Article  Google Scholar 

  13. Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72, 2729–2730 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Francisco Tomás y Valiente 11, 28049, Madrid, Spain

    Ángela Fernández, Ana M. González, Julia Díaz & José R. Dorronsoro

Authors
  1. Ángela Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ana M. González
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julia Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José R. Dorronsoro
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VŠB-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs(MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, 120-749, Seoul, Korea

    Sung-Bae Cho

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fernández, Á., González, A.M., Díaz, J., Dorronsoro, J.R. (2012). Diffusion Maps for the Description of Meteorological Data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-28942-2_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28941-5

  • Online ISBN: 978-3-642-28942-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation