Benchmarking a reduced multivariate polynomial pattern classifier

Kar Ann Toh, Quoc Long Tran, Dipti Srinivasan

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119 Citations (Scopus)


A novel method using a reduced multivariate polynomial model has been developed for biometric decision fusion where simplicity and ease of use could be a concern. However, much to our surprise, the reduced model was found to have good classification accuracy for several commonly used data sets from the Web. In this paper, we extend the single output model to a multiple outputs model to handle multiple class problems. The method is particularly suitable for problems with small number of features and large number of examples. Basic component of this polynomial model boils down to construction of new pattern features which are sums of the original features and combination of these new and original features using power and product terms. A linear regularized least-squares predictor is then built using these constructed features. The number of constructed feature terms varies linearly with the order of the polynomial, instead of having a power law in the case of full multivariate polynomials. The method is simple as it amounts to only a few lines of Matlab code. We perform extensive experiments on this reduced model using 42 data sets. Our results compared remarkably well with best reported results of several commonly used algorithms from the literature. Both the classification accuracy and efficiency aspects are reported for this reduced model.

Original languageEnglish
Pages (from-to)740-755
Number of pages16
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number6
Publication statusPublished - 2004 Jun

Bibliographical note

Funding Information:
2. Data from the Attitudes Towards Smoking Legislation Survey-Metropolitan Toronto 1988, which was funded by NHRDP (Health and Welfare Canada), were collected by the Institute for Social Research at York University for Dr. Linda Pederson and Dr. Shelley Bull.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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