稀疏判别分析报告.docVIP

稀疏判别分析 摘要:针对流形嵌入降维方法中在高维空间构建近邻图无益于后续工作，以及不容易给近邻大小和热核参数赋合适值的问题，提出一种稀疏判别分析算法（seda）。首先使用稀疏表示构建稀疏图保持数据的全局信息和几何结构，以克服流形嵌入方法的不足；其次，将稀疏保持作为正则化项使用fisher判别准则，能够得到最优的投影。在一组高维数据集上的实验结果表明，seda是非常有效的半监督降维方法。 关键词:判别分析；稀疏表示；近邻图；稀疏图  sparse discriminant analysis  chen xiao.dong1*, lin huan.xiang2 1．school of information and engineering, zhejiang radio and television university, hangzhou zhejiang 310030, china; 2．school of information and electronic engineering,zhejiang university of science and technology, hangzhou zhejiang 310023, china abstract: methods for manifold embedding exists in the following issues: on one hand, neighborhood graph is constructed in such the high-dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction is generally difficult to be assigned. to address these problems, a novel semi-supervised dimensionality reduction algorithm called sparse discriminant analysis (seda) is proposed. firstly, seda sets up a sparse graph to preserve the global information and geometric structure of the data based on sparse representation. secondly, it applies both sparse graph and fisher criterion to seek the optimal projection. experiments on a broad range of data sets show that seda is superior to many popular dimensionality reduction methods. methods for manifold embedding have the following issues: on one hand, neighborhood graph is constructed in such high.dimensionality of original space that it tends to work poorly; on the other hand, appropriate values for the neighborhood size and heat kernel parameter involved in graph construction are generally difficult to be assigned. to address these problems, a new semi.supervised dimensionality reduction algorithm called sparse discriminant analysis (seda) was proposed. firstly, seda set up a sparse graph to preserve the global information and geometric structure of the data based on sparse r