Linear discriminant analysis (LDA) is an important feature extraction method. This paper proposes an improved linear discriminant analysis method, which redefines the within-class scatter matrix and introduces the normalized parameter to control the bias and variance of eigenvalues. In addition, it makes the between-class scatter matrix to weight and avoids the overlapping of neighboring class samples. Some experiments for the improved algorithm presented by us are performed on the ORL, FERET and YALE face databases, and it is compared with other commonly used methods. Experimental results show that the proposed algorithm is the effective.
A New Reduced-Rank Linear Discriminant Analysis Method and Its Applications
