Please use this identifier to cite or link to this item: https://doi.org/10.1109/TPAMI.2004.3
Title: Benchmarking a reduced multivariate polynomial pattern classifier
Authors: Toh, K.-A.
Tran, Q.-L.
Srinivasan, D. 
Keywords: Machine learning
Multivariate polynomials
Parameter estimation
Pattern classification
Pattern recognition
Issue Date: Jun-2004
Source: Toh, K.-A., Tran, Q.-L., Srinivasan, D. (2004-06). Benchmarking a reduced multivariate polynomial pattern classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence 26 (6) : 740-755. ScholarBank@NUS Repository. https://doi.org/10.1109/TPAMI.2004.3
Abstract: 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.
Source Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/55197
ISSN: 01628828
DOI: 10.1109/TPAMI.2004.3
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