关键词: 离散度矩阵, 线性判别分析, 降维, 最大边界准则, 子空间, 小样本问题

Abstract: By the analysis of the classical Linear Discriminant Analysis(LDA)and Maximum Margin Criterion(MMC)methods, a supervised dimensionality reduction by in-depth learning within scatters of classes which called Similarity Subspace Embedding(SSE)was proposed. A deep study on the within class scatter matrix was made. The divergences of the subspace of each class were obtained by subspace learning. This approach could get abundant information between class scatter matrixes, and then get a better low dimensional space. Compared with the MMC method, the SSE method was more adequate for the class of data learning, while avoiding the small sample problem of the LDA method. Experimental results on AR face image, Coil data set and handwriting showed that the proposed method had a higher recognition rate compared with other three classic methods, which showed the effectiveness of the proposed method.

Key words: dimensionality reduction, MMC, scatter matrix, subspace, LDA