Abstract: A novel document classification algorithm based on manifold regularization framework, which is called MI_I} RLSC, is presented to resolve high dimensional document classification. In the proposed MLI}RLSC, a nearest neighbor graph was constructed and the intrinsic geometrical structure of the sample space was taken as a manifold regularization term,then it was incorporated into the objective function of the multivariate linear regression to extract lower dimen- sional space. The classification and predication in the lower dimensional feature space are implemented with kNN. Ai- ming to extract effective features for the multi-class problem, MLD-RLSC can make use of all labeled samples. Experi- mental results on Reuters 21578 dataset demonstrate that the proposed algorithm is of higher classification accuracy and faster running speed.

Key words: LDE, Manifold learning, Next categorization, kNN, Manifold regularization