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Comparative Assessment of the Robustness of Missing Data Imputation Through Generative Topographic Mapping

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

The incompleteness of data is a most common source of uncertainty in real-world Data Mining applications. The management of this uncertainty is, therefore, a task of paramount importance for the data analyst. Many methods have been developed for missing data imputation, but few of them approach the problem of imputation as part of a general data density estimation scheme. Amongst the latter, a method for imputing and visualizing multivariate missing data using Generative Topographic Mapping was recently presented. This model and some of its extensions are tested under different experimental conditions. Its performance is compared to that of other missing data imputation techniques, thus assessing its relative capabilities and limitations.

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References

  1. Olinsky, A., Chen, S., Harlow, L.: The Comparative Efficacy of Imputation Methods for Missing Data in Structural Equation Modelling. Eur. J. Oper. Res. 151, 53–79 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  2. Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing Value Estimation Methods for DNA Microarrays. Bioinformatics 17, 520–525 (2001)

    Article  Google Scholar 

  3. Vicente, D., Vellido, A., Martí, E., Comas, J., Rodriguez-Roda, I.: Exploration of the Ecological Status of Mediterranean Rivers: Clustering, Visualizing and Reconstructing Streams Data Using Generative Topographic Mapping. In: Zanasi, A., Ebecken, N.F.F., Brebbia, C.A. (eds.) Data Mining V. W.I.T. Transactions on Information and Communication Technologies, vol. 30, pp. 121–130 (2004)

    Google Scholar 

  4. Cooke, M.P., Green, P.D., Josifovski, L., Vizinho, A.: Robust Automatic Speech Recognition with Missing and Unreliable Acoustic Data. Speech Commun. 34, 267–285 (2001)

    Article  MATH  Google Scholar 

  5. Schafer, J.L.: Analysis of Incomplete Data. Chapman & Hall, London (1997)

    Book  MATH  Google Scholar 

  6. Sun, Y., Tiňo, P., Nabney, I.: GTM-Based Data Visualization with Incomplete Data. Technical Report, NCRG, Aston University, UK (2001)

    Google Scholar 

  7. Olier, I., Vellido, A.: Assessing the Robustness of Missing Data Imputation Through Generative Topographic Mapping. ACIA Newsletter 31, 14–20 (2004)

    Google Scholar 

  8. Bishop, C.M., Svensén, M., Williams, C.K.I.: GTM: The Generative Topographic Mapping. Neural Comput. 10, 215–234 (1998)

    Article  Google Scholar 

  9. Dempster, A.P., Laird, M.N., Rubin, D.B.: Maximum Likelihood from Incomplete Data Via the EM Algorithm. J. Roy. Statist. Soc. Ser. B 39, 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  10. Kohonen, T.: Self-organizing Maps. Springer, Berlin (1995)

    Google Scholar 

  11. Bishop, C.M., Svensén, M., Williams, C.K.I.: Developments of the Generative Topographic Mapping. Neurocomputing 21, 203–224 (1998)

    Article  MATH  Google Scholar 

  12. Vellido, A., El-Deredy, W., Lisboa, P.J.G.: Selective Smoothing of the Generative Topographic Mapping. IEEE Trans. Neural Networks 14, 847–852 (2003)

    Article  Google Scholar 

  13. Mackay, D.J.C.: A Practical Bayesian Framework for Back-Propagation Networks. Neural Comput. 4, 448–472 (1992)

    Article  Google Scholar 

  14. Carreira-Perpiñan, M.A.: Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints. In: Solla, S.A., Leen, T.K., Müller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 414–420. MIT Press, Cambridge (2000)

    Google Scholar 

  15. Ghahramani, Z., Jordan, M.I.: Learning from Incomplete Data. Technical Report, AI Laboratory, MIT (1994)

    Google Scholar 

  16. Hunt, L., Jorgensen, M.: Mixture Model Clustering for Mixed Data with Missing Information. Comput. Statist. Data Anal. 41, 429–440 (2003)

    Article  MathSciNet  Google Scholar 

  17. Wang, H.X., Zhang, Q.B., Luo, B., Wei, S.: Robust Mixture Modelling Using Multivariate t-Distribution with Missing Information. Pattern Recognit. Lett. 25, 701–710 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computing Languages and Systems (LSI), Polytechnic University of Catalonia (UPC), C. Jordi Girona, 1-3., 08034, Barcelona, Spain

    Iván Olier & Alfredo Vellido

Authors
  1. Iván Olier
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  2. Alfredo Vellido
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Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Department of Computer Architecture and Computer Technology, University of Granada,  

    Alberto Prieto

  3. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

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© 2005 Springer-Verlag Berlin Heidelberg

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Olier, I., Vellido, A. (2005). Comparative Assessment of the Robustness of Missing Data Imputation Through Generative Topographic Mapping. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_96

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  • DOI: https://doi.org/10.1007/11494669_96

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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