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Using self-organizing maps to visualize high-dimensional data

机译：使用自组织地图可视化高维数据

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摘要

Understanding relationships in high-dimension datasets requires proper data visualization. Two examples of high-dimension data are major-element geochemical and hyperspectral data. Major-element geochemical data consists of eleven oxide measurements for each sample. Well-known correlations exist for these types of data, i.e., the negative relationship between SiO2, and MgO; other more subtle relationships are rarely apparent. Hyperspectral data is by definition high-dimension data consisting of upwards of 100 + discrete measurements of the electromagnetic spectrum for a material. Hyperspectral data are a significant challenge to interpret when evaluating information for heterogeneous materials such as rocks. Self-organizing maps (SOMs) provide insight into complex relationships in high-dimension datasets while preserving the inherent topological relations and simultaneously producing a statistical model of the dataset. Another benefit of SOMs is their generation of composite vectors which can be analyzed to extract the relative importance of each component during classification. The veracity of SOMs is demonstrated using two datasets from the Spanish peaks intrusive complex of south-central Colorado including major-element geochemical and hyperspectral measurements. (c) 2004 Elsevier Ltd. All :rights reserved.
机译：了解高维数据集中的关系需要适当的数据可视化。高维数据的两个例子是主要元素地球化学和高光谱数据。主要元素地球化学数据包括每个样品的11个氧化物测量值。对于这些类型的数据，存在众所周知的相关性，即SiO2与MgO之间的负相关。其他更微妙的关系很少见。从定义上讲，高光谱数据是高维数据，包括材料的100多个离散的电磁频谱测量值。在评估诸如岩石之类的非均质材料的信息时，高光谱数据是一个巨大的挑战。自组织映射（SOM）可以洞悉高维数据集中的复杂关系，同时保留固有的拓扑关系并同时生成数据集的统计模型。 SOM的另一个好处是可以生成复合矢量，可以对其进行分析以提取分类过程中每个组件的相对重要性。使用来自科罗拉多州中南部的西班牙峰侵入复合体的两个数据集（包括主要元素地球化学和高光谱测量）证明了SOM的准确性。 （c）2004 Elsevier Ltd.保留所有权利。

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