Nonlinear feature extraction based on centroids and kernel functions
A nonlinear feature extraction method is presented which can reduce the data dimension down to the number of classes, providing dramatic savings in computational costs. The dimension reducing nonlinear transformation is obtained by implicitly mapping the input data into a feature space using a kernel function, and then finding a linear mapping based on an orthonormal basis of centroids in the feature space that maximally separates the between-class relationship. The experimental results demonstrate that our method is capable of extracting nonlinear features effectively so that competitive performance of classification can be obtained with linear classifiers in the dimension reduced space. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
nonlinear feature extraction kernel functions cluster structure dimension reduction kernel orthogonal centroid method linear discriminant analysis pattern classification support vector machines