Pattern Recognition Based on Multidimensional Nonlinear Schur Parametrization

Abstract

Feature extraction is one of the most important stages of pattern recognition. In the paper, a second-degree nonlinear Schur parametrization is proposed as a method of extraction of features from non-Gaussian and non-stationary time-series. The nonlinear algorithm is derived from the linear Schur parametrization. The experimental pattern recognition, using several well-known classifiers, is performed on UCI ML repository benchmark data: 60-dimensional sonar digital data set. The classification accuracy for nonlinear Schur parameterization as feature extraction is compared to the results obtained for the linear Schur parametrization and other popular feature extraction methods. The use of a nonlinear parametrization method causes a significant increase in the classification accuracy, comparing to linear case, with a relatively moderate – as for multidimensional nonlinear algorithm– increase in the number of features.