关键词: 线性判别分析, 小样本问题, 镜像图像, 零空间, 类间离散度, 类内离散度

Abstract: Because the Small Sample Size (SSS) problem usually leads to singularity of the within-class scatter matrix, there will be an ill-posed problem in solving the generalized character equation. An improved algorithm based on former ones was proposed. It introduced mirror image to enlarge sample capacity and adopted the method of the null space of the within-class scatter matrix Sw to get the best solution of Fisher criterion. Experimental results on ORL face database and Yale face database show that the proposed algorithm is more effective than traditional Linear Discriminant Analysis (LDA), Independent Components Analysis (ICA) and 2-Dimensional Symmetric Principal Component Analysis (2DSPCA).

Key words: Linear Discriminant Analysis (LDA), Small Sample Size (SSS) problem, mirror image, null space, between-class scatter matrix, within-class scatter matrix