Nonlinear dimensionality reduction method is more sensitive to the noise in the high-dimensional data, resulting in relatively poor final results of classification. In order to make up for its shortcomings, this paper proposes a method in the premise of using the maximum likelihood estimation method to estimate the intrinsic dimension of the sample data, which combines the isomapetric mapping with the principal component analysis. On the one hand, the method enables the original data to maintain its geometry in the high dimensional space, on the other hand, the method can eliminate the influence of noise on the dimensionality reduction results, eventually making the low-dimensional data as much as possible to maintain inherent characteristics of the original sample data sets. Experimental demonstrations show that the results of combination method is better than separate isometric mapping and separate principal component analysis.%由于非线性降维方法对高维数据中存在的噪声比较敏感，导致最终的分类效果比较差。为了弥补其不足，在首先使用极大似然估计方法估测出样本数据本征维度的前提下，提出一种结合等距特征映射与主成分分析的方法。一方面能够使原始数据保持其在高维空间的几何结构，另一方面可以消除噪声对降维结果的影响，最终使得低维数据尽可能的保持原始样本数据集的内在特征。通过实验论证表明，该组合方法的效果比单独直接使用等距特征映射和主成分分析算法的效果都要好。
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