Angular Brushing of Extended Parallel Coordinates
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Revealing Structure within Clustered Parallel Coordinates Displays
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Topological Landscapes: A Terrain Metaphor for Scientific Data
IEEE Transactions on Visualization and Computer Graphics
Brushing of Attribute Clouds for the Visualization of Multivariate Data
IEEE Transactions on Visualization and Computer Graphics
Glimmer: Multilevel MDS on the GPU
IEEE Transactions on Visualization and Computer Graphics
Comparing measures of sparsity
IEEE Transactions on Information Theory
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Handbook of Blind Source Separation: Independent Component Analysis and Applications
Interactive Data Visualization: Foundations, Techniques, and Applications
Interactive Data Visualization: Foundations, Techniques, and Applications
IEEE Transactions on Visualization and Computer Graphics
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
Progressive parallel coordinates
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
Piecewise laplacian-based projection for interactive data exploration and organization
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visualizing high-dimensional structures by dimension ordering and filtering using subspace analysis
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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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.