核岭回归的邻域保持最大间隔分析的人脸识别

邻域保持嵌入是局部线性嵌入的线性近似,强调保持数据流形的局部结构.改进的最大间隔准则重视数据流形的判别和几何结构,提高了对数据的分类性能.文中提出的核岭回归的邻域保持最大间隔分析既保持流形的局部结构,又使不同类别的数据保持最大间隔,以此构建算法的目标函数.为了解决数据流形高度非线性化的问题,算法采用核岭回归计算特征空间的变换矩阵.先求解数据样本在核子空间中降维映射的结果,再解得核子空间.在标准人脸数据库上的实验表明该算法正确有效,并且识别性能优于普通的流形学习算法.

Face Recognition Using Neighborhood Preserving Maximal Margin Analysis of Kernel Ridge Regression

Neighborhood preserving embedding is a linear approximation to locally linear embedding,and it emphasizes preserving the local structure of the data manifold.The modified maximal margin criterion focuses on the discriminant and geometrical structure of the data manifold,and it improves the classification performance of the data.An algorithm is proposed called neighborhood preserving maximal margin analysis of kernel ridge regression.It preserves the local structure of the manifold and maximizes margins between the data of different classes to construct the objective function.As the data manifold is highly nonlinear,the kernel ridge regression is adopted to calculate the transformation matrix.The mapped results of the data samples are obtained by the proposed algorithm in the kernel subspace firstly,then the kernel subspace is obtained.The experimental results on the standard face database demonstrate that the proposed algorithm is correct and effective.Moreover,it achieves better performance than the popular manifold learning algorithms.