Traditional isometric feature mapping algorithm does not consider the classification labels of data when reducing the dimensionality, and cannot produce a mapping matrix ranging from high dimensions to lower dimensions after the dimensionality being reduced, and it cannot fit the situation of multi-class clusters as well, so it cannot be directly used for classification.In light of these problems, we use neighbourhood component analysis ( NCA) to replace the multidimensional scaling analysis ( MDS) , introduce eigenvector as the input matrix, and propose an isometric feature mapping algorithm aiming at classification, called NC-ISOMAP.In the process of dimensionality reduction, NC-ISOMAP can obtain an ideal low dimensional project matrix, which makes the data become more separate between classes and more compact within a class after lowering the dimensionality.Experimental results show that NC-ISOMAP is able to achieve quite good dimensionality reduction result and classification performance, and has a better robustness in different datasets.%传统的等距特征映射算法在降维时未考虑数据的类别标签，降维后不能够产生从高维到低维的映射矩阵，且不适用于多个类簇的情况，不能直接用于分类。针对这几个问题利用近邻元分析方法取代多维尺度分析法，并且引入特征向量作为输入矩阵，提出一种以分类为目的的等距特征映射算法（ NC-ISOMAP）。降维时获取理想的低维投影矩阵，使降维后类间数据更加分开，类内数据更加紧凑。实验结果表明NC-ISOMAP算法能够取得很好的降维效果和分类性能，并在不同的数据集中有着较好的鲁棒性。
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