Feature extraction, discriminant analysis, and classification rule are three crucial issues for face recognition. This paper presents one method, named GaborfaceSVM, to handle three issues together. For feature extraction, we utilize the Gabor wavelet transform on grey face image to extract Gaborfaces. A Modified Enhanced Fisher Discriminant model is used to reinforce discriminant power of Gabo...

This paper investigates the use of the dimensionality-reduction techniques weighted linear discriminant analysis (WLDA), and weighted median fisher discriminant analysis (WMFD), before probabilistic linear discriminant analysis (PLDA) modeling for the purpose of improving speaker verification performance in the presence of high inter-session variability. Recently it was shown that WLDA techniqu...

In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which ...

Kernel Fisher discriminant analysis (KFDA) has been proposed for nonlinear binary classification. It is a hybrid method of the classical Fisher linear discriminant analysis and a kernel machine. Experimental results have shown that the KFDA performs slightly better in terms of prediction error than the popular support vector machines and is a strong competitor to the latter. However, there is v...

We are often interested in casting classification and clustering problems as a regression framework, because it is feasible to achieve some statistical properties in this framework by imposing some penalty criteria. In this paper we illustrate optimal scoring, which was originally proposed for performing the Fisher linear discriminant analysis by regression, in the application of unsupervised l...

To efficiently cope with the high dimensionalities and complex nonlinear variations of face images in face recognition task, a novel manifold adaptive kernel local Fisher discriminant analysis algorithm is proposed in this paper. The core idea of this algorithm is as follows: First, the local manifold structure of the face image is modeled by a nearest neighbor graph. Then, an original input ke...

To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonlinear FDA or NFDA), it is sufficient to expand the input data by computing the output of a basis of F when applied to it [1–4]. The solution to NFDA can then be found like in the linear case by solving a generalized eigenvalue pr...

One of the applications of the discriminant analysis on microarray data is to classify patient and normal samples based on gene expression values. The analysis is especially important in medical trials and diagnosis of cancer subtypes. The main contribution of this paper is to propose a simple Fisher-type discriminant method on gene selection in microarray data. In the new algorithm, we calcula...

We propose to investigate test statistics for testing homogeneity based on kernel Fisher discriminant analysis. Asymptotic null distribution under null hypothesis is derived, and consistency against fixed alternatives is assessed. Finally, experimental evidence of the performance of the proposed approach on artificial data and on a speaker verification task is provided.

Objective: We have performed Fisher’s discriminant analysis to the EEG data of experimental subjects who were drunk on alcohol. The subjects of these experiments were recorded anonymously. The data has been used for feature extraction and classification decisions in order to determine the part of head electrode’s categories after alcohol intake and to explore the changes of EEG features. The ca...