< Abstract >In this paper, a novel approach to feature extraction for classification is proposed based directly on the decision boundaries. We note that feature extraction is equivalent to retaining informative features or eliminating redundant features, thus first the terms "discriminantly
In this paper, through a series of specific examples, we illustrate some characteristics encountered in analyzing high dimensional multispectral data. The increased importance of the second order statistics in analyzing high dimensional data is illustrated, as is the shortcoming of classifiers such as the minimum distance classifier which rely on first order variations alone. We also illustrate how inaccurate estimation of first and second order statistics e.g., from use of training sets which are too small, affects the performance of a classifier. Recognizing the importance of second order statistics on the one hand, but the increased difficulty in perceiving and comprehending information present in statistics derived from high dimensional data on the other, we propose a method to aid visualization of high dimensional statistics using a color coding scheme.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.