LTSA (local tangent space alignment) is a recently proposed method for manifold learning, which can efficiently learn nonlinear embedding low-dimensional coordinates of high-dimensional data, and can also reconstruct high dimensional coordinates from embedding coordinates. But it ignores the label information conveyed by data samples, and can not be used for classification directly. In this paper, a transductive manifold classification method, called QLAT (LDA/
ransductive classifier) is presented, which is based on LTSA and TCM-KNN (transduction confidence machine-k nearest neighbor). In the algorithm, local low-dimensional coordinates is constructed using 2-stage LDA/QR method, which not only utilize the label information of sample data, but also conquer the singularity problem of traditional LDA, then the global low-dimensional embedding manifold is obtained by local affine transforms, finally TCM-KNN method is used for classification on the low-dimensional manifold. Experiments on labeled and unlabeled mixed data set illustrate the effectiveness of the method.