基于局部边界鉴别分析的人脸识别

该文基于谱图理论和流形学习提出了局部边界鉴别分析(LMDA)的降维方法。在近邻保持投影的基础上，LMDA方法减少了同类数据间由于线性投影而带来的重构误差，同时保留了类内相似度图的拉普拉斯矩阵的完整性。另一方面，通过构造一个与类内相似图对应的类外代价图，LMDA可以扩大两者间的边界。在人脸识别中与其他方法的对比实验表明提出的算法能有效提升近邻保持投影的性能。

Local Marginal Discriminant Analysis for Face Recognition

A novel dimensionality reduction method called Local Marginal Discriminant Analysis (LMDA) is proposed in this paper based on spectral graph theory and manifold learning. Based on Neighborhood Preserving Projections (NPP), the reconstruction distortion in the intra-class caused by linear projections is minimized, and at the same time the integrity of the Laplacian matrix of the intra-class graph is kept, and ‘margin’ between inter-class and intra-class is also maximized by constructing a weighted ‘compactness’ nearest-neighbor graphs and a counterpart ‘penalty’ graph. Finally, the numerical experimental results compared to other methods show that LMDA outperforms NPP.