Record Details

Title:
Non-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF
Abstract:
Stacks of elemental distribution images acquired by XRF can be difficult to interpret, if they contain high degrees of redundancy and components differing in their quantitative but not qualitative elemental composition. Factor analysis, mainly in the form of Principal Component Analysis (PCA), has been used to reduce the level of redundancy and highlight correlations. PCA, however, does not yield physically meaningful representations as they often contain negative values. This limitation can be overcome, by employing factor analysis that is restricted to non-negativity. In this paper we present the first application of the Python Matrix Factorization Module (pymf) on XRF data. This is done in a case study on the painting Saul and David from the studio of Rembrandt van Rijn. We show how the discrimination between two different Co containing compounds with minimum user intervention and a priori knowledge is supported by Non-Negative Matrix Factorization (NMF).
Imprint:
Bristol, IOP Publ., 2014
Journal Information:
J. Phys. Conf. Ser., 499, 012013 (2014)
ISSN:
1742-6588
1742-6596
External related publications:
10.1088/1742-6596/499/1/012013
WOS:000338041300013 (WOS)
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 Record created 2016-10-11, last modified 2017-09-14

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