Towards High-dimensional Data Analysis in Air Quality Research

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dc.contributor.authorEngel, Danielen_US
dc.contributor.authorHummel, Mathiasen_US
dc.contributor.authorHoepel, Florianen_US
dc.contributor.authorBein, Keithen_US
dc.contributor.authorWexler, Anthonyen_US
dc.contributor.authorGarth, Christophen_US
dc.contributor.authorHamann, Bernden_US
dc.contributor.authorHagen, Hansen_US
dc.contributor.editorB. Preim, P. Rheingans, and H. Theiselen_US
dc.date.accessioned2015-02-28T15:30:25Z
dc.date.available2015-02-28T15:30:25Z
dc.date.issued2013en_US
dc.description.abstractAnalysis of chemical constituents from mass spectrometry of aerosols involves non-negative matrix factorization, an approximation of high-dimensional data in lower-dimensional space. The associated optimization problem is non-convex, resulting in crude approximation errors that are not accessible to scientists. To address this shortcoming, we introduce a new methodology for user-guided error-aware data factorization that entails an assessment of the amount of information contributed by each dimension of the approximation, an effective combination of visualization techniques to highlight, filter, and analyze error features, as well as a novel means to interactively refine factorizations. A case study and the domain-expert feedback provided by the collaborating atmospheric scientists illustrate that our method effectively communicates errors of such numerical optimization results and facilitates the computation of high-quality data factorizations in a simple and intuitive manner.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12097en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12097en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.5.5 [Pattern Recognition]en_US
dc.subjectDesign Methodologyen_US
dc.subjectFeature evaluation and selectionen_US
dc.titleTowards High-dimensional Data Analysis in Air Quality Researchen_US
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