Rich unlabeled data contains valuable information, which is useful for classification. Using information efficiently to improve the accuracy of classification is the major purpose of semi-supervised learning. This paper proposes a kind of semi-supervised classification approach called Semi-Supervised Discriminant Analysis that is based on Manifold Distance, SSDA. The intra-class neighbors, the inter-class neighbors, and the total neighbors of a selected point can be determined by the proposed manifold distance. The similarity between these neighbors and the point can be defined based on the manifold distance. The object function is defined using the similarity. As the experiments operated on the database ORL and YALE show, compared with the existing algorithms, the proposed algorithm can improve the accuracy of classified algorithms based on distance. When dealing with nonlinear dimensionality reduction problem, the Kernel SSDA (namely, kernel semi-supervised discriminant analysis based on manifold distance) is proposed. Also, the experimental results show the efficiency of this algorithm.