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 …

Several dimensionality reduction methods based on the max-min idea have been proposed
in recent years and can obtain good classification performance. In this paper, inspired by the …

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 …

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 …

Dimensionality reduction methods have been successfully employed for face recognition.
Among the various dimensionality reduction algorithms, linear (Fisher) discriminant analysis …

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

Dimensionality reduction is often recommended to handle high dimensional data before
performing the tasks of visualization and classification. So far, large families of …

In this paper, we proposed a linear discriminant approach, namely global–local Fisher
discriminant analysis (GLFDA) that explicitly considers both the local and global discriminant …

Linear discriminant analysis (LDA) and its variants are popular supervised dimension
reduction methods, which have been widely used to handle high-dimensional data. Since …

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