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Towards High‐dimensional Data Analysis in Air Quality Research
Author(s) -
Engel D.,
Hummel M.,
Hoepel F.,
Bein K.,
Wexler A.,
Garth C.,
Hamann B.,
Hagen H.
Publication year - 2013
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12097
Subject(s) - computer science , computation , visualization , dimension (graph theory) , domain (mathematical analysis) , matrix decomposition , approximation error , non negative matrix factorization , simple (philosophy) , multidimensional data , filter (signal processing) , factorization , data mining , algorithm , theoretical computer science , mathematics , computer vision , mathematical analysis , philosophy , eigenvalues and eigenvectors , physics , epistemology , quantum mechanics , pure mathematics
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.