Graph-Optimized Line Discriminant Analysis for Face Recognition

This paper develops a supervised discriminant technique, called graph embedding discriminant analysis (GEDA), for dimensionality reduction of high-dimensional data in small sample size problems. GEDA can be seen as a linear approximation of a ...

Manifold learning algorithms mainly focus on discovering the intrinsic low-dimensional manifold embedded in the high-dimensional Euclidean space. Among them, locally linear embedding (LLE) is one of the most promising dimensionality reduction methods. ...

Linear discriminant analysis (LDA) is a powerful dimensionality reduction technique, which has been widely used in many applications. Although, LDA is well-known for its discriminant capability, it clearly does not capture the geometric structure of the ...