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Functional analysis techniques to improve similarity matrices in discrimination problems

Author

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  • González, Javier
  • Muñoz, Alberto

Abstract

In classification problems an appropriate choice of the data similarity measure is a key step to guarantee the success of discrimination procedures. In this work, we propose a general methodology to transform the available data similarity S, incorporating the data labels, to improve the performance of discrimination procedures. We will focus on the case when S is asymmetric. We study the precise connection between similarity matrices and integral operators that will allow the evaluation of the transformed matrix on test points. The proposed methodology is used in several simulated and real experiments where the performance of several discrimination techniques is improved.

Suggested Citation

  • González, Javier & Muñoz, Alberto, 2013. "Functional analysis techniques to improve similarity matrices in discrimination problems," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 120-134.
    • Handle: RePEc:eee:jmvana:v:120:y:2013:i:c:p:120-134
    DOI: 10.1016/j.jmva.2013.04.013
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    References listed on IDEAS

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    1. J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March.
    2. Manuel Ammann & Stephan Suss, 2009. "Asymmetric dependence patterns in financial time series," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 703-719.
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