Abstract 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.
LI Yong-Zhou,LUO Da-Yong,LIU Shao-Qiang. Face Recognition Using Neighborhood Preserving Maximal Margin Analysis of Kernel Ridge Regression[J]. , 2010, 23(1): 23-28.
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