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中文摘要：
基于排列技术，提出一种新的流形学习的方法——局部坐标排列（Local Coordinates Alignment，LCA）.LCA首先计算局部坐标作为每一个局部邻域的表达，然后通过在全局中排列从而得到最优嵌入.实验验证了LCA的有效性.与LE相比，所提出的LCA更贴近于流形学习的局部保存和全局优化的思想.
英文摘要：
A novel algorithm for manifold learning named local coordinates alignment （LCA） was proposed based on the alignment technique. LCA first computes the local coordinates as the representations for each local neighborhood, and then obtains the optimal embeddings by aligning these local coordinates in globality, The experimental results show the effectiveness of the proposed algorithm, In LCA, the idea of local preservation and global optimization is more apparent than that in LE （Laplacian eigenmap）.
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