Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images, limiting the performance of neural networks in decision-making processes. To address this issue, we propose a method that approximates the dataset topology by constructing adjacency graphs using the Uniform Manifold Approximation and Projection. This approach captures nonlinear correlations within the data, significantly improving the performance of machine learning algorithms, particularly in processing Hyperspectral Images (HSI) from X-ray transmission spectroscopy. This technique not only preserves the global structure of the data but also enhances feature separability, leading to more accurate and robust classification results.

翻译：传统的特征提取与投影技术（如主成分分析）难以充分表征X射线透射多能级图像，从而限制了神经网络在决策过程中的性能。为解决此问题，我们提出一种方法，通过使用均匀流形逼近与投影构建邻接图来近似数据集拓扑结构。该方法能够捕捉数据中的非线性关联，显著提升机器学习算法（特别是在处理X射线透射光谱学产生的高光谱图像时）的性能。此技术不仅保留了数据的全局结构，还增强了特征可分性，从而获得更精确且鲁棒的分类结果。