Self-Organizing Maps
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A Next Step: Visualizing Errors and Uncertainty
IEEE Computer Graphics and Applications
Steerable, Progressive Multidimensional Scaling
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Hierarchical Splatting of Scattered Data
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Trajectory-based visual analysis of large financial time series data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
IEEE Transactions on Visualization and Computer Graphics
Class visualization of high-dimensional data with applications
Computational Statistics & Data Analysis
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Selecting good views of high-dimensional data using class consistency
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Assisted descriptor selection based on visual comparative data analysis
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Visual Analytics for model-based medical image segmentation: Opportunities and challenges
Expert Systems with Applications: An International Journal
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The analysis of high-dimensional data is an important, yet inherently difficult problem. Projection techniques such as Principal Component Analysis, Multi-dimensional Scaling and Self-Organizing Map can be used to map high-dimensional data to 2D display space. However, projections typically incur a loss in information. Often, uncertainty exists regarding the precision of the projection as compared with its original data characteristics. While the output quality of these projection techniques can be discussed in terms of aggregate numeric error values, visualization is often helpful for better understanding the projection results. We address the visual assessment of projection precision by an approach integrating an appropriately designed projection precision measure directly into the projection visualization. To this end, a flexible projection precision measure is defined that allows the user to balance the degree of locality at which the measure is evaluated. Several visual mappings are designed for integrating the precision measure into the projection visualization at various levels of abstraction. The techniques are implemented in an interactive system, including methods supporting the user in finding appropriate settings of relevant parameters. We demonstrate the usefulness of the approach for visual analysis of classified and unclassified high-dimensional data sets. We show how our interactive precision quality visualization system helps to examine the preservation of original data properties in projected space.