Many biometric applications such as face recognition involve data with a large number of
features [1–3]. Analysis of such data is challenging due to the curse-ofdimensionality [4, 5] …

Linear discriminant analysis (LDA) is the most widely used supervised dimensionality
reduction approach. After removing the null space of the total scatter matrix S t via principal …

Dimensionality reduction is a critical technology in the domain of pattern recognition, and
linear discriminant analysis (LDA) is one of the most popular supervised dimensionality …

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 …

Existing supervised and semi-supervised dimensionality reduction methods utilize training
data only with class labels being associated to the data samples for classification. In this …

Fisher criterion has achieved great success in dimensionality reduction. Two representative
methods based on Fisher criterion are Fisher Score and Linear Discriminant Analysis (LDA) …

Spectral methods have recently emerged as a powerful tool for dimensionality reduction and
manifold learning. These methods use information contained in the eigenvectors of a data …

The dimensionality reduction methods based on linear embedding, such as neighborhood
preserving embedding (NPE), sparsity preserving projections (SPP) and collaborative …

Abstract Principal Component Analysis (PCA) is an example of a successful unsupervised
statistical dimensionality reduction method, especially in small sample size problems …

A large family of algorithms-supervised or unsupervised; stemming from statistics or
geometry theory-has been designed to provide different solutions to the problem of …