Abstract Multivariate data obtained using, for instance, Laser-Induced Breakdown Spectroscopy (LIBS), are quite bulky and complex. Advanced processing of spectroscopic data demands a multidisciplinary approach, covering not only modern machine learning tools but also a deep understanding of underlying physical mechanisms. Dimension reduction and visualization of large datasets is a task of significant interest in the spectroscopic data processing. Commonly employed linear techniques (e.g., Principal Component Analysis, PCA) cannot explain the correlations of higher-order which are present in the data. Even more, computational cost and memory limitations become way more relevant considering the size of “modern” LIBS data (millions of high-dimensional spectra). Methods based on Artificial Neural Networks (ANN) seem suitable for this task, and based on their success, they are given considerable attention within the spectroscopic community. We propose a new methodology based on Restricted Boltzmann Machine (ANN method) for dimensionality reduction of spectroscopic data and compare it to standard PCA. As an extension to successful reconstruction, we demonstrate a generation of new (unseen) spectra by the RBM model trained on a large spectroscopic dataset. This data generation is of great use not only for the extending measured datasets but also as a proper training state's confirmation of the model.

中文翻译：

---

用于大光谱数据降维的受限玻尔兹曼机方法

摘要 使用例如激光诱导击穿光谱 (LIBS) 获得的多变量数据非常庞大和复杂。光谱数据的高级处理需要采用多学科方法，不仅涵盖现代机器学习工具，还需要深入了解潜在的物理机制。大数据集的降维和可视化是光谱数据处理中的一项重要任务。常用的线性技术（例如，主成分分析，PCA）无法解释数据中存在的高阶相关性。更重要的是，考虑到“现代”LIBS 数据（数百万个高维光谱）的大小，计算成本和内存限制变得更加重要。基于人工神经网络 (ANN) 的方法似乎适合这项任务，基于他们的成功，他们在光谱学界受到了相当大的关注。我们提出了一种基于受限玻尔兹曼机（ANN 方法）的新方法，用于光谱数据的降维，并将其与标准 PCA 进行比较。作为成功重建的扩展，我们通过在大型光谱数据集上训练的 RBM 模型展示了一代新的（看不见的）光谱。这种数据生成不仅对于扩展测量数据集非常有用，而且还可以作为模型的正确训练状态的确认。作为成功重建的扩展，我们通过在大型光谱数据集上训练的 RBM 模型展示了一代新的（看不见的）光谱。这种数据生成不仅对于扩展测量数据集非常有用，而且还可以作为模型的正确训练状态的确认。作为成功重建的扩展，我们通过在大型光谱数据集上训练的 RBM 模型展示了一代新的（看不见的）光谱。这种数据生成不仅对于扩展测量数据集非常有用，而且还可以作为模型的正确训练状态的确认。