Literature DB >> 27041779

Non-negative matrix analysis in x-ray spectromicroscopy: choosing regularizers.

Rachel Mak1, Stefan M Wild2, Chris Jacobsen3.   

Abstract

In x-ray spectromicroscopy, a set of images can be acquired across an absorption edge to reveal chemical speciation. We previously described the use of non-negative matrix approximation methods for improved classification and analysis of these types of data. We present here an approach to find appropriate values of regularization parameters for this optimization approach.

Entities:  

Keywords:  multivariate statistical analysis; non-negative matrix analysis; optimization; x-ray microscopy; x-ray spectromicroscopy

Year:  2016        PMID: 27041779      PMCID: PMC4817849          DOI: 10.1063/1.4937528

Source DB:  PubMed          Journal:  AIP Conf Proc        ISSN: 0094-243X


  4 in total

1.  Learning the parts of objects by non-negative matrix factorization.

Authors:  D D Lee; H S Seung
Journal:  Nature       Date:  1999-10-21       Impact factor: 49.962

2.  Cluster analysis of soft X-ray spectromicroscopy data.

Authors:  M Lerotic; C Jacobsen; T Schäfer; S Vogt
Journal:  Ultramicroscopy       Date:  2004-07       Impact factor: 2.689

3.  MANTiS: a program for the analysis of X-ray spectromicroscopy data.

Authors:  Mirna Lerotic; Rachel Mak; Sue Wirick; Florian Meirer; Chris Jacobsen
Journal:  J Synchrotron Radiat       Date:  2014-07-31       Impact factor: 2.616

4.  Non-negative matrix analysis for effective feature extraction in X-ray spectromicroscopy.

Authors:  Rachel Mak; Mirna Lerotic; Holger Fleckenstein; Stefan Vogt; Stefan M Wild; Sven Leyffer; Yefim Sheynkin; Chris Jacobsen
Journal:  Faraday Discuss       Date:  2014-07-25       Impact factor: 4.008

  4 in total

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