A novel combination of machine learning algorithms is proposed for the differentiation of distinct spectra in a large electron energy loss spectroscopy spectrum image (EELS-SI) dataset. For clustering of the EEL spectra including similar fine structures in an efficient space, linear and nonlinear dimensionality reduction methods are used to project the EEL spectra onto a low-dimensional space. Then, a density-based clustering algorithm is applied to distinguish the meaningful data clusters. By applying this strategy to various experimental EELS-SI datasets, differentiation of several groups of EEL spectra representing specific fine structures was achieved. It is possible to investigate particular fine structures by averaging all of the spectra in each cluster. Also, the spatial distributions of each cluster in the scanning regions can be observed, which enables investigation of the locations of different fine structures in materials. This method does not require any prior knowledge, i.e., it is a data-driven analysis; therefore, it can be applied to any hyperspectral image.

提出了一种新的机器学习算法组合，用于区分大型电子能量损失光谱图像 (EELS-SI) 数据集中的不同光谱。为了在有效空间中对包含相似精细结构的 EEL 光谱进行聚类，使用线性和非线性降维方法将 EEL 光谱投影到低维空间。然后，应用基于密度的聚类算法来区分有意义的数据簇。通过将此策略应用于各种实验 EELS-SI 数据集，实现了代表特定精细结构的几组 EEL 光谱的区分。通过平均每个簇中的所有光谱，可以研究特定的精细结构。此外，可以观察到每个簇在扫描区域中的空间分布，这使得研究材料中不同精细结构的位置成为可能。这种方法不需要任何先验知识，即它是一种数据驱动的分析；因此，它可以应用于任何高光谱图像。