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Overview of manifold learning techniques for the investigation of disruptions on JET

机译：用于研究JET中断的多种学习技术概述

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Identifying a low-dimensional embedding of a high-dimensional data set allows exploration of the data structure. In this paper we tested some existing manifold learning techniques for discovering such embedding within the multidimensional operational space of a nuclear fusion tokamak. Among the manifold learning methods, the following approaches have been investigated: linear methods, such as principal component analysis and grand tour, and nonlinear methods, such as self-organizing map and its probabilistic variant, generative topographic mapping. In particular, the last two methods allow us to obtain a low-dimensional (typically two-dimensional) map of the high-dimensional operational space of the tokamak. These maps provide a way of visualizing the structure of the high-dimensional plasma parameter space and allow discrimination between regions characterized by a high risk of disruption and those with a low risk of disruption. The data for this study comes from plasma discharges selected from 2005 and up to 2009 at JET. The self-organizing map and generative topographic mapping provide the most benefits in the visualization of very large and high-dimensional datasets. Some measures have been used to evaluate their performance. Special emphasis has been put on the position of outliers and extreme points, map composition, quantization errors and topological errors.
机译：识别高维数据集的低维嵌入可以探索数据结构。在本文中，我们测试了一些现有的流形学习技术，以发现这种嵌入在核聚变托卡马克的多维操作空间中。在流形学习方法中，已研究了以下方法：线性方法（例如主成分分析和盛大游览）和非线性方法（例如自组织图及其概率变体，生成地形图）。特别地，后两种方法允许我们获得托卡马克高维操作空间的低维（通常是二维）图。这些图提供了一种可视化高维等离子体参数空间结构的方法，并允许在以高破坏风险为特征的区域和低破坏风险为特征的区域之间进行区分。这项研究的数据来自JET从2005年至2009年选择的等离子放电。自组织地图和生成式地形图在超大型和高维数据集的可视化中提供了最大的好处。一些措施已用于评估其性能。特别强调了异常点和极端点的位置，地图组成，量化误差和拓扑误差。

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