Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

The current discriminant analysis method design is generally independent of classifiers, thus
the connection between discriminant analysis methods and classifiers is loose. This paper …

A generalized discriminant analysis based on a new optimization criterion is presented. The
criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) …

Over the years, many discriminant analysis (DA) algorithms have been proposed for the
study of high-dimensional data in a large variety of problems. Each of these algorithms is …

Linear discriminant analysis (LDA) is well known as a powerful tool for discriminant analysis.
In the case of a small training data set, however, it cannot directly be applied to high …

A nonparametric method of discriminant analysis is proposed. It is based on nonparametric
extensions of commonly used scatter matrices. Two advantages result from the use of the …

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 …

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach.
The algorithms of LDA usually perform well under the following two assumptions. The first …

Discriminant analysis has been used for decades to extract features that preserve class
separability. It is commonly defined as an optimization problem involving covariance …

Abstract Linear Discriminant Analysis (LDA) is a popular data-analytic tool for studying the
class relationship between data points. A major disadvantage of LDA is that it fails to …