半监督边缘判别嵌入与局部保持的维度约简

为了对高维数据进行降维处理，提出了半监督学习的边缘判别嵌入与局部保持的维度约简算法．通过最小化样本与其所属类别的中心点之间的距离，使得样本在投影子空间中能够保持其领域的拓扑结构；再通过最大化不同类别边缘间的距离，使得类别间的分离度在投影子空间中得到增强．实验结果表明：半监督边缘判别嵌入与局部保持的维度约简算法能够获得初始特征空间的较好的投影子空间．

Semi Supervised Marginal Discriminant Embedding and Local Preserving for Dimensionality Reduction

In order to reduce the dimension of high-dimensional data, raised edge semi-supervised marginal discriminant embedding and local preserving algorithm for dimensionality reduction is proposed. By minimizing the distance between sample and the center of its category, the local topology of samples is maintained in the projection subspace. And by maximizing the distance between the edges of different categories, the inter scatter of classes is increased in the projection subspace. Experimental results show that the dimensionality reduction algorithm of semi supervised marginal discriminant embedding and local preserving can get a better projection subspace of the initial feature space.