为了将线性局部切空间排列算法发展为有监督的学习算法，提出了一种正交判别的线性局部切空间排列算法。该算法首先利用样本数据的类别信息计算类间散度矩阵，然后再通过对原算法的目标函数进行修改来建立新的优化问题。在解出投影子空间的基础上，再通过进行正交化来得到投影的正交子空间。在两个标准人脸数据库上进行的实验表明，由于该算法使用了局部切空间来表示数据样本所在流形的局部几何结构，不仅融合了判别信息和正交化技术，并且兼顾了局部几何结构和判别结构的保持，因此提高了识别能力。

In order to develop linear local tangent space alignment to supervised learning algorithm,an algorithm called orthogonal discriminant linear local tangent space alignment is proposed.The algorithm makes use of class information of the data samples to compute the interclass scatter matrix.Then we modify the objective function of the original algorithm,constructing the new optimization problem.Moreover,on this basis,the algorithm orthogonalizes the subspace to obtain the orthogonal one.The effectiveness of the algorithm has been verified on two standard face databases.With local tangent space representing for local geometrical structure of the manifold of the data samples，the algorithm fuses discriminant information and orthogonal technique to preserve the local geometrical structure and discriminant structure,and the algorithm improves the recognition performance.