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...

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...

Subspace learning based pattern recognition methods have attracted considerable interests in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of p...

In radar HRRP target recognition, the quality and quantity of Discriminant Information (DI), which one is more important? Accompanied with this issue, the paper proceeds to delve into DI analysis, and accordingly, three fundamental DI extraction models are proposed, i.e., PGA, PIB and AIB. Among these models, PIB and AIB both aim to obtain Between-class DI (B-DI) from individual standpoints whi...

Subspace learning based pattern recognition methods have attracted considerable interests in recent years, including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and some extensions for 2D analysis. However, a disadvantage of all these approaches is that they perform subspace analysis directly on the reshaped vector or matrix of p...

Subspace learning techniques have been extensively used for dimensionality reduction (DR) in many pattern classification problem domains. Recently, methods like Subclass Discriminant Analysis (SDA) and Clusteringbased Discriminant Analysis (CDA), which use subclass information for the discrimination between the data classes, have attracted much attention. In parallel, important work has been ac...