中文摘要：

传统的Isomap算法仅侧重于当前数据的分析，不能提供由高维空间到低维空间的快速直接映射，因此无法用于特征提取和高维数据检索．针对这一问题，文中提出一种基于Isomap的快速数据检索算法．该算法能够快速得到新样本的低维嵌入坐标，并基于此坐标检索与输入样本最相似的参考样本．在典型测试集上的实验结果表明，该算法在实现新样本到低维流形快速映射的同时，能较好保留样本的近邻关系．

英文摘要：

The traditional lsomap algorithm emphasizes analyzing the manifold structure of the existing dataset. It can not provide fast and direct mapping of a new sample from high dimensional space to low dimensional space, so the traditional Isomap algorithm can not be used for feature extraction and high-dimensional data retrieval. In this paper, a fast mapping Isomap algorithm is proposed, by which the low-dimensional coordinates of a new sample can be calculated with relatively low computational complexity, and the most similar sample of the query sample can be retrieved based on such low-dimensional coordinates. Experimental results on typical benchmark datasets demonstrate that the proposed algorithm accomplishes the task of fast mapping with well preserving of the neighborhood relationship.