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000168474 1001_ $$0P:(DE-H253)PIP1007955$$aAlfeld, Matthias$$b0$$eCorresponding Author
000168474 245__ $$aNon-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF
000168474 260__ $$aBristol$$bIOP Publ.$$c2014
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000168474 520__ $$aStacks of elemental distribution images acquired by XRF can be difficult to interpret, if they contain high degrees of redundancy and components differing in their quantitative but not qualitative elemental composition. Factor analysis, mainly in the form of Principal Component Analysis (PCA), has been used to reduce the level of redundancy and highlight correlations. PCA, however, does not yield physically meaningful representations as they often contain negative values. This limitation can be overcome, by employing factor analysis that is restricted to non-negativity. In this paper we present the first application of the Python Matrix Factorization Module (pymf) on XRF data. This is done in a case study on the painting Saul and David from the studio of Rembrandt van Rijn. We show how the discrimination between two different Co containing compounds with minimum user intervention and a priori knowledge is supported by Non-Negative Matrix Factorization (NMF).
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000168474 7001_ $$0P:(DE-H253)PIP1018777$$aWahabzada, Mirwaes$$b1
000168474 7001_ $$0P:(DE-HGF)0$$aBauckhage, Christian$$b2
000168474 7001_ $$0P:(DE-HGF)0$$aKersting, Kristian$$b3
000168474 7001_ $$0P:(DE-H253)PIP1006746$$aWellenreuther, Gerd$$b4
000168474 7001_ $$0P:(DE-H253)PIP1002716$$aFalkenberg, Gerald$$b5
000168474 773__ $$0PERI:(DE-600)2166409-2$$a10.1088/1742-6596/499/1/012013$$gVol. 499, p. 012013 -$$p012013$$tJournal of physics / Conference Series$$v499$$x1742-6596$$y2014
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000168474 9141_ $$y2014
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