Efficient Extraction and Classification of Spectra

Computer Science

Scientific paper

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Scientific paper

Imaging spectrometers deliver very large amounts are data which call for automatic summarisation for exploratory data analysis. In the frequent absence of ground truth for planetary data, unsupervised analysis methods can provide unbiased information about the data. In this work, we investigate the use of unsupervised analysis based on non-negative matrix approximation [3, 4] combined with subsequent classification [2] to provide scientists with succinct summaries. Since typically there often is no ground truth to compare to, unsupervised rather than supervised methods allow to extract new information from data sets. We designed particularly efficient methods to cope with the large data volumes which are typical for this type of instrument.

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