Using self-organizing maps to visualize high-dimensional data

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.