用自编码器生成2D光谱数据，解决标注样本少的问题。
Synthetic generation of 2D data records based on Autoencoders
- 基于自编码器框架生成合成2D光谱数据
- 加入合成数据后分类准确率显著提升
- 适合标注数据稀缺的光谱分析场景
气相色谱-离子迁移谱（GC-IMS）是一种双分离分析技术，广泛用于气体样品组分识别，通过分离并分析组分到达时间获得二维谱图。此类数据虽信息丰富，但因标注数据集有限，给数据驱动分析带来挑战。本研究提出一种基于深度学习自编码器的新型2D谱图合成方法。该方法虽应用于GC-IMS数据，但可推广至任意二维光谱测量场景，当在标注数据集上进行组分分类时，引入合成记录显著提升了分类性能，验证了其在克服机器学习中数据集限制方面的潜力。
原文摘要 · Abstract (English)
Gas Chromatography coupled with Ion Mobility Spectrometry (GC-IMS) is a dual-separation analytical technique widely used for identifying components in gaseous samples by separating and analysing the arrival times of their constituent species. Data generated by GC-IMS is typically represented as two-dimensional spectra, providing rich information but posing challenges for data-driven analysis due to limited labelled datasets. This study introduces a novel method for generating synthetic 2D spectra using a deep learning framework based on Autoencoders. Although applied here to GC-IMS data, the approach is broadly applicable to any two-dimensional spectral measurements where labelled data are scarce. While performing component classification over a labelled dataset of GC-IMS records, the addition of synthesized records significantly has improved the classification performance, demonstrating the method's potential for overcoming dataset limitations in machine learning frameworks.
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