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Boundary Methods (BMs) are a collection of tools used for distribution analysis. This paper explores the theoretical and complexity issues associated with using BMs for Feature Set Evaluation (FSE). First we show the theoretical relationship between Overlap Sum (OS), the BM measure of class separability, and Bayes error (e). This relationship demonstrates the utility of using BMs for FSE. Next, we investigate complexity issues associated with using BMs for FSE and compare with other techniques used for FSE.

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This paper investigates the use of Boundary Methods (BMs), a collection of tools used for distribution analysis, as a method for estimating the number of modes associated with a given data set. Model order information of this type is required by several pattern recognition applications. The BM technique provides a novel approach to this parameter timation problem and is comparable in terms of both accuracy and computations to other popular mode estimation techniques currently found in the literature and automatic target recognition applications. This paper explains the methodology used in the BM approach to mode estimation. Also, this paper quickly reviews other common mode estimation techniques and describes the empirical investigation used to explore the relationship of the BM technique to other mode estimation techniques. Specifically, the accuracy and computational efficiency of the BM technique are compared quantitatively to the a mixture of Gaussian (MOG) approach and a k-means approach to model order estimation. The stopping criteria of the MOG and k-means techniques is the Akaike Information Criteria (AIC).