Abstract: Linear Laplacian discrimination (LLD) as a non-linear feature extraction method has obtained very extensive applications. However, LDD suffers from the small sample size problem (SSS) and/or the type of the sample space when it is used. In order to circumvent such shortcomings, a contextual-distance metric based Laplacian maximum margin criterion (CLMMC) is proposed in this paper by using contextual-distance metric and integrating maximum margain criterion (MMC) into the LLD. The proposed criterion can obviously decrease the dependence on the sample space since the contextual-distance metric focuses more on intrinsic structure of a cluster of samples than on its type. And it is of higher adaptability and efficiency to use the new algorithm to compute contextual-distance metric and applying QR-discomposition when high-dimensional vector data are dealt with. The basic properties of CLMMC and its relation to LLD are also discussed. The experimental results indicate the above advantages of the CLMMC.