Adaptative training of the non-linear single-layer perceptron can lead to the Euclidean distance classifier and later to the standard Fisher linear discriminant function. On the way between these two classifiers one has a regularized discriminant analysis. That is equivalent to the “weight decay” regularization term added to the cost function. Thus early stopping plays a role of regularization ...

We present an enhanced direct linear discriminant analysis (EDLDA) solution to effectively and efficiently extract discriminatory features from high dimensional data. The EDLDA integrates two types of class-wise weighting terms in estimating the average within-class and between-class scatter matrices in order to relate the resulting Fisher criterion more closely to the minimization of classific...

We propose a linear discriminant analysis method. In this method, every discriminant vector, except for the first one, is worked out by maximizing a Fisher criterion defined in a transformed space which is the null space of the previously obtained discriminant vectors. All of these discriminant vectors are used for dimension reduction. We also propose two algorithms to implement the model. Base...

A method for the linear discrimination of two classes is presented. It searches for the discriminant direction which maximizes the Patrick-Fisher (PF) distance between the projected class-conditional densities. It is a nonparametric method, in the sense that the densities are estimated from the data. Since the PF distance is a highly nonlinear function, we propose a recursive optimization proce...

Gears are the most essential parts in rotating machinery. So gear fault modes diagnosis and levels classification are very important in engineering practice. This paper present a novel method in gear fault recognition and identification using Fisher discriminant analysis (FDA) due to FDA can reduct dimension when analyse signal. The real data collected from a gearbox test rig is used to validat...