中文摘要：

针对一般流形学习算法在学习高光谱数据的多流形结构时存在的不足，提出一种基于线性局部与全局保持嵌入（LLGPE）的多流形学习算法．对于分布在不同流形上的高维观测数据，利用LLGPE算法学习每类分组数据的内蕴特征；然后通过遗传算法搜索每类数据的本质维数；最后根据重构误差最小化准则确定样本所属的类别．在HYDICE高光谱数据集上的分类识别实验结果表明，文中算法能够有效地揭示高维空间中数据的内蕴几何结构；在每类随机选取2，4，6个训练样本的情况下，该算法的总体分类精度比其他流形学习算法分别提高了约3．5％，6．9％和7．2％，且分类精度也有明显的提高．

英文摘要：

Traditional manifold learning methods assume that hyperspectral data may reside on one single manifold, but data from different classes may reside on different manifolds of possible different intrinsic dimensions. In order to explore multiple low-dimensional manifolds in hyperspectral images, a multi-manifold learning algorithm based on local and global preserving embedding （LLGPE） is proposed. First, the manifolds of different classes are learned by LLGPE for each class separately, and the data are projected onto low-dimensional spaces. Then, the optimal dimensionality of each class is founded by genetic algorithm （GA） from the viewpoint of classification. At last, classification is performed under a minimum reconstruction error based classifier. The experimental results on the HYDICE hyperspectral data show the effectiveness of the proposed algorithm, when 2, 4 and 6 samples of each class are randomly selected for training and 90 samples of each class for testing, the overall accuracy of the proposed algorithm is improved by 3. 5%, 6.9% and 7. 2% respectively, as compared with other methods.