一种相似子空间嵌入算法
A similarity subspace embedding algorithm

摘要： 通过对经典的线性判别分析(Linear Discriminant Analysis, LDA)及最大边界准则(Maximum Margin Criterion, MMC)方法的分析,提出一种类内子空间深入学习的监督降维方法——相似子空间嵌入(Similarity Subspace Embedding, SSE),对类内离散度矩阵进行深入学习,得到每类的类内离散度子空间,通过对所有类内离散度子空间的学习,获得信息更为丰富的类间离散度矩阵,进而得到更好的低维空间。与MMC方法相比,SSE方法对类内数据学习更充分,同时避免了LDA方法存在的小样本问题。在AR人脸图像、Coil数据集及手写体上的试验结果表明,与其它三种相关的经典方法相比, SSE方法具有较高的识别率,说明了该方法的有效性。
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