A novel statistical linear discriminant analysis for image matrix: two-dimensional fisherfaces
Ming Li, Baozong Yuan · 2005
In the pattern recognition field, how to extract the proper features is a very important problem. In recent year, the statistical methods have been researched widely and many methods for feature extraction have been developed, such as, PCA, ICA, nonlinear PCA and etc. But the image always need be transformed to a ID vector in the traditional statistical methods. This paper proposed a novel linear discriminant analysis for image matrix, which achieved better result than the traditional ones. Experiments also proof our method is effective.