Dimensionality reduction is an important aspect in the pattern classification literature, and
linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction …

Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction
problems as a preprocessing step for machine learning and pattern classification …

The classical linear discriminant analysis (LDA) algorithm has three primary drawbacks, ie,
small sample size problem, sensitivity to noise and outliers, and inability to deal with multi …

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 …

We propose in this paper a parametric regularized locality preserving projections (LPP)
method for face recognition. Our objective is to regulate the LPP space in a parametric …

We propose a novel algorithm for supervised dimensionality reduction named manifold
partition discriminant analysis (MPDA). It aims to find a linear embedding space where the …

Video-based facial expression recognition is a challenging problem in computer vision and
human-computer interaction. To target this problem, texture features have been extracted …

A novel classification method using ℓ2, 1-norm based regression is proposed in this paper.
The ℓ2, 1-norm based loss function is robust to outliers or large variations distributed in the …

Semisupervised dimensionality reduction has been attracting much attention as it not only
utilizes both labeled and unlabeled data simultaneously, but also works well in the situation …

Robust descriptor-based subspace learning with complex data is an active topic in pattern
analysis and machine intelligence. A few researches concentrate the optimal design on …