Nearest Neighbor Convex Hull Classification of Fisher Discriminant Features
Jiang Wen · 2007
Feature extraction based on Fisher criteria is a branch of pattern recognition.Foley-Sammon algorithm and the Uncorrelated Fisher Linear Discriminant Analysis(ULDA)are the classic two of those correlative methods.As preprocessors,they usually cooperate with other classification algorithm such as the minimum distance classifier,the nearest neighbor classifier or support vector machines(SVMs).In this paper,the results of them are used as inputs to a new classification method named the nearest neighbor convex hull(NNCH)classifier,which takes the convex hull of one class training data as a new unit class.The test sample will belong to the class of the nearest convex hull in the feature space.Nonlinearity,no parameters and multi-class applicability are the characters of NNCH.The experiments compared with the other cooperators mentioned above,indicate the good performance of the proposed methods.