An Efficient Feature Extraction Method, Global Between Maximum and Local Within Minimum, and Its Applications

摘要

Feature extraction plays an important role in preprocessing procedure indealing with small sample size problems. Considering the fact that LDA, LPP, and many other existing methods are confined to one case of the data set. To solvethis problem, we propose an efficient method in this paper, named globalbetween maximum and local within minimum. It not only considers theglobal structure of the data set, but also makes the best of the local geometry ofthe data set through dividing the data set into four domains. This methodpreserves relations of the nearest neighborhood, as well as demonstrates anexcellent performance in classification. Superiority of the proposed methodin this paper is manifested in many experiments on data visualization, facerepresentative, and face recognition.