一种光谱分析中的降维方法

在可见/近红外光谱分析中，提取光谱数据中的有用信息是建立稳健准确模型的前提。ISOMAP是一种有效的提取数据本真维的降维方法，但对噪声和邻域参数都比较敏感。提出了一种改进的ISOMAP有监督降维方法，利用光谱数据本身的相关性指导邻域图的构建，降低对噪声和邻域参数的敏感程度，以正确表达数据的邻域结构。采用该方法对两组光谱数据降维并进行PLS建模，结果表明，改进后的算法消弱了邻域大小的影响，提取出的本真维数更小，同时提高了模型精度。

A Dimension Reduction Method Applied in Spectrum Analysis

It is the premise of establishing stable and accurate model to extract useful information from spectrum data in Vis/NIR spectrum analysis technology. ISOMAP is a dimension reduction method, and can effectively extract the intrinsic low dimension from high dimensional data, but is sensitive to noise and neighborhood parameter. In this paper, an improved ISOMAP algorithm, called supervised dimension reduction, is proposed. It guides the construction of the neighborhood graph using correlation owned by spectrum data, and reduces sensitivity to noise and neighborhood parameter. The algorithm was applied to two datasets, and then PLS models were established. The experiment results indicated that the improved algorithm was less sensitive to the neighborhood size and more robust and more topologically stable. In addition, smaller dimension was extracted, and the model precision was improved at the same time.