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

In graph embedding based methods, we usually need to manually choose the nearest
neighbors and then compute the edge weights using the nearest neighbors via L2 norm (eg …

In this survey we give a short introduction into appearance-based object recognition. In
general, one distinguishes between two different strategies, namely local and global …

In this paper, we propose a new discriminant locality preserving projections based on
maximum margin criterion (DLPP/MMC). DLPP/MMC seeks to maximize the difference …

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 …

When the feature dimension is larger than the number of samples the small sample-size
problem occurs. There is great concern about it within the face recognition community. We …

Many pattern recognition applications involve the treatment of high-dimensional data and
the small sample size problem. Principal component analysis (PCA) is a common used …

Maximum margin criterion (MMC) based feature extraction is more efficient than linear
discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to …

High-dimensional data and the small sample size problem occur in many modern pattern
classification applications such as face recognition and gene expression data analysis. To …

Recently, bidirectional principal component analysis (BDPCA) has been proven to be an
efficient tool for pattern recognition and image analysis. Encouraging experimental results …