Fisher Large Margin Linear Classifier
Fisher大间距线性分类器

Fisher linear discriminant analysis(LDA),a well-known feature extraction method,searches for the projection axes on which the data samples from different classes are far from each other while requiring data samples of the same class to be close to each other.Large margin classifier(LMC),also referred as linear support vector machine,de finds a project direction onto which two classes of the samples projected reach maximal margin.With combination of advantages of both LDA and LMC,the paper develops a novel linear projection classfication algorithm,called Fisher large margin linear classifier.The underlying idea is that an optimal discrimiant vector wbest is found along which the samples of high dimensional input space are projected such that the margin is maximized while within-class scatter is kept as small as possible.In addition,relations to other classifiers are explored in theory in this paper.Finally,the proposed method is tested on ORL face database and FERET face database.The experimental results show that the proposed classifier outperforms other linear classifiers.