Discriminative analysis of dimensionality reduction methods for pattern recognition

Summary form only given. In the paper, the comparison of discriminative capabilities of conventional dimensionality reduction methods and the integration of a dimensionality reduction module and recognizer design with minimum classification error rate are discussed. Conventionally, principal component analysis (PCA) and Fisher´s linear discriminant (FLD) are two most popular and widely used dimensionality reduction methods for pattern recognition. However, the objectives of these two methods are quite different. PCA basically tries to faithfully keep the original data representation but FLD tries to separate data distribution of different classes. It therefore seems that FLD can provide more discriminative characteristics to pattern recognition than PCA. However, a completely optimal feature extractor can never be anything but an optimal recognizer. It is only when constraints are placed on the classifier that one can formulate nontrivial problems. We apply a minimum error formulation (MEF) to integrate the design of dimensionality reduction module and pattern recognizer. The experimental results show that such an integration provides very good recognition performance on a data set of hand-written Chinese characters even when the feature number has been significantly reduced.