We propose a new method of discriminant analysis, called High Dimensional Discriminant Analysis (HHDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. Thus, HDDA reduces the dimension for each class independently and regularizes class conditional covariance matrices in order to adapt the Gaussian framework to high dimensio...

background: epilepsy is a severe disorder of the central nervous system that predisposes  the person to recurrent seizures. fifty million people worldwide suffer from  epilepsy; after alzheimer's and stroke, it is the third widespread nervous disorder. objective: in this paper, an algorithm to detect the onset of epileptic seizures  based on the analysis of brain electrical signals (eeg) has be...

Linear discriminant analysis with binary response is considered when the predictor is a functional random variableX = {Xt, t ∈ [0, T ]}, T ∈ R. Motivated by a food industry problem, we develop a methodology to anticipate the prediction by determining the smallest T ∗, T ∗ ≤ T , such that X∗ = {Xt, t ∈ [0, T ∗]} and X give similar predictions. The adaptive prediction concerns the observation of ...

This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indictors. The students are intended to be placed into three reference groups: advanced, regular, and remedial. Various discriminant techniques, including Fisher’s d...

Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...

In this article, it was planned that we shall discuss Discriminant and Cluster analysis. While preparing the discussions for both topics, there was an overwhelming large amount of information and thus we shall concentrate on Discriminant analysis only and leave Cluster analysis to Biostatistics 304. Discriminant analysis (DA) was the traditional statistical technique used for differentiating gr...

Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several two-dimensional LDA (2DLDA) methods have been proposed in recent years. Among which, the Y2DLDA method, introduced by Ye et al. (2005), is an important development. The idea is to utilize the underlying 2D data ...

Linear Discriminant Analysis (LDA) and its nonlinear version Kernel Discriminant Analysis (KDA) are well-known and widely used techniques for supervised feature extraction and dimensionality reduction. They determine an optimal discriminant space for (non)linear data projection based on certain assumptions, e.g. on using normal distributions (either on the input or in the kernel space) for each...

Relational discriminant analysis is based on a proximity description of the data. Instead of features, the similarities to a subset of the objects in the training data are used for representation. In this paper we will show that this subset might be small and that its exact choice is of minor importance. Moreover, it is shown that linear or non-linear methods for feature extraction based on mul...

CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by extension CCA, does not use label information. Fisher Discriminant Analysis uses label information to find informative projections, which can be more informative in supervised learning settings. We show ...