In machine learning, linear discriminant analysis (LDA) is a popular dimension reduction
method. In this paper, we first provide a new perspective of LDA from an information theory …

Dimensionality reduction is often needed in many applications due to the high
dimensionality of the data involved. In this paper, we first analyze the scatter measures used …

Linear discriminant analysis (LDA) is one of the important techniques for dimensionality
reduction, machine learning, and pattern recognition. However, in many applications …

Linear discriminant analysis (LDA) is a widely used technique for supervised feature
extraction and dimensionality reduction. LDA determines an optimal discriminant space for …

Linear Discriminant Analysis (LDA) is a well-known method for dimensionality reduction and
classification. LDA in the binaryclass case has been shown to be equivalent to linear …

Several variants of Linear Discriminant Analysis (LDA) have been investigated to address
the vanishing of the within-class scatter under projection to a low-dimensional subspace in …

The traditional linear discriminant analysis (LDA) is a classical dimensionality reduction
method. But there are two problems with LDA. One is the small-sample-size (SSS) problem …

High-dimensional data are common in many domains, and dimensionality reduction is the
key to cope with the curse-of-dimensionality. Linear discriminant analysis (LDA) is a well …

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique,
which is widely used for many purposes. However, conventional LDA is sensitive to outliers …

We propose an eigenvector-based heteroscedastic linear dimension reduction (LDR)
technique for multiclass data. The technique is based on a heteroscedastic two-class …