In order to better identify the fault of rotor system,one new method based on local fuzzy clustering margin fisher discriminant (LFCMFD) was proposed. For each point on manifold, the farthest point in local neighborhood and the nearest point outside local neighborhood usually constituted the local margin. LFCMFD introduced fuzzy cluster analysis algorithm, eliminated the influence of pseudo-mar...

Median Fisher Discriminator(MFD) used the class median vector is more effective than Linear discriminant analysis(LDA). However, MFD only captures global geometrical structure information of the data and ignores the geometrical structure information of local data point. In this paper, we introduce a linear approach, called Maximal Margin Local Preserving Median Fisher Discriminant Analysis (MML...

The objective of this research was to examine whether the Holmes-Rahe Social Readjustment Rating Scale, a life event scale, can be used to identify suicide attempters. The Holmes-Rahe Social Readjustment Rating Scale's ability to identify suicide attempters was tested in 1183 subjects (478 suicide attempters, 197 psychiatric inpatients, and 508 healthy controls) using the Fisher Linear Discrimi...

We give a probabilistic interpretation of canonical correlation (CCA) analysis as a latent variable model for two Gaussian random vectors. Our interpretation is similar to the probabilistic interpretation of principal component analysis (Tipping and Bishop, 1999, Roweis, 1998). In addition, we can interpret Fisher linear discriminant analysis (LDA) as CCA between appropriately defined vectors.

Many linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD) methods are based on the restrictive assumption that the data are homoscedastic. In this paper, we propose a new KFD method called heteroscedastic kernel weighted discriminant analysis (HKWDA) which has several appealing characteristics. First, like all kernel methods, it can handle nonlinearity efficiently in...

A modified linear discriminant analysis technique for speaker verification, referred to here as normalized discriminant analysis (NDA), is presented. Using this technique it is possible to design an efficient linear classifier with very limited training data and to generate normalized discriminant scores with comparable magnitudes for different classifiers. The NDA technique is applied to a cla...

Abstract Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study invest...