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Kernelized inner product-based discriminant analysis for interval data

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Abstract

This work presents an approach based on the kernelized discriminant analysis to classify symbolic interval data in nonlinearly separable problems. It is known that the use of kernels allows to map implicitly data into a high-dimensional space, called feature space; computing projections in this feature space results in a nonlinear separation in the input space that is equivalent to linear separating function in the feature space. In this work, the kernel matrix is obtained based on kernelized interval inner product. Experiments with synthetic interval data sets and an application with a Brazilian thermographic breast database demonstrate the usefulness of this approach.

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Acknowledgements

The authors wish to thank the Editor-in-Chief, Professor Sameer Singh and two anonymous referees for their constructive comments on an earlier version of this manuscript. This research work was partially supported by a CNPq, CAPES and FACEPE agency from Brazil.

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Authors and Affiliations

  1. Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n - Cidade Universitaria (Campus Recife), Recife, PE, 50.740-560, Brazil

    D. C. F. Queiroz & R. M. C. R. Souza

  2. Departamento de Estatística - CCEN, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n - Cidade Universitaria (Campus Recife), Recife, PE, 50.740-540, Brazil

    F. J. A. Cysneiros

  3. Departamento de Engenharia Mecânica, CTG, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, 1235 - Cidade Universitaria (Campus Recife), Recife, PE, 50.670-901, Brazil

    M. C. Araujo

Authors
  1. D. C. F. Queiroz
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  2. R. M. C. R. Souza
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  3. F. J. A. Cysneiros
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  4. M. C. Araujo
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Correspondence to D. C. F. Queiroz.

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Queiroz, D.C.F., Souza, R.M.C.R., Cysneiros, F.J.A. et al. Kernelized inner product-based discriminant analysis for interval data. Pattern Anal Applic 21, 731–740 (2018). https://doi.org/10.1007/s10044-017-0601-3

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  • DOI: https://doi.org/10.1007/s10044-017-0601-3

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