Fisher's linear discriminant analysis (LDA) is popular for dimension reduction and extraction
of discriminant features in many pattern recognition applications, especially biometric …

It is well-known that the applicability of linear discriminant analysis (LDA) to high-
dimensional pattern classification tasks such as face recognition often suffers from the so …

Linear discriminant analysis (LDA) is widely studied in statistics, machine learning, and
pattern recognition, which can be considered as a generalization of Fisher's linear …

Linear discriminant analysis (LDA) is a popular method in pattern recognition and is
equivalent to Bayesian method when the sample distributions of different classes are obey …

A critical issue of applying Linear Discriminant Analysis (LDA) is both the singularity and
instability of the within-class scatter matrix. In practice, particularly in image recognition …

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 …

In this paper, we present a scalable and exact solution for probabilistic linear discriminant
analysis (PLDA). PLDA is a probabilistic model that has been shown to provide state-of-the …

Conclusion In this paper, we have proposed a new regularization scheme to overcome the
small sample size problem in the linear discriminant analysis. A regularized discriminant …

This paper presents a median–mean line based discriminant analysis (MMLDA) technique
for dimensionality reduction. Taking the negative effect on the class-mean caused by outliers …

Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal
linear transformation that maximizes the class separability. However, in undersampled …