Towards High-dimensional Data Analysis in Air Quality Research

Analysis 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...

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Published inComputer graphics forum Vol. 32; no. 3pt1; pp. 101 - 110
Main Authors Engel, D., Hummel, M., Hoepel, F., Bein, K., Wexler, A., Garth, C., Hamann, B., Hagen, H.
Format Journal Article
LanguageEnglish
Published Oxford, UK Blackwell Publishing Ltd 01.06.2013
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Online AccessGet full text
ISSN0167-7055
1467-8659
DOI10.1111/cgf.12097

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Abstract Analysis 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.
AbstractList Analysis 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.
Analysis 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 [PUBLICATION ABSTRACT].
Author Hoepel, F.
Engel, D.
Wexler, A.
Hamann, B.
Garth, C.
Hummel, M.
Bein, K.
Hagen, H.
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Snippet Analysis of chemical constituents from mass spectrometry of aerosols involves non‐negative matrix factorization, an approximation of high‐dimensional data in...
Analysis of chemical constituents from mass spectrometry of aerosols involves non-negative matrix factorization, an approximation of high-dimensional data in...
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SubjectTerms Analysis
Approximation
Assessments
Atmospheric aerosols
Atmospherics
Chemicals
Computer graphics
Environmental monitoring
Error analysis
Factorization
I.5.5 [Pattern Recognition]: Design Methodology-Feature evaluation and selection
Mathematical analysis
Optimization
Outdoor air quality
Scientists
Studies
Title Towards High-dimensional Data Analysis in Air Quality Research
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Volume 32
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