Find distance function, hide model inference.
Liu, Jingjing.
Brown, Eli T.
Chang, Remco.
2011.
- Faced with a large, high-dimensional dataset, many turn to data analysis approaches that they understand less well than the domain of their data. An expert's knowledge can be leveraged into many types of analysis via a domain-specific distance function, but creating such a function is not intuitive to do by hand. We have created a system that shows an initial visualization, adapts to user feedback, ... read moreand produces a distance function as a result. Specifically, we present a multidimensional scaling (MDS) visualization and an iterative feedback mechanism for a user to affect the distance function that informs the visualization without having to adjust the parameters of the visualization directly. An encouraging experimental result suggests that using this tool, data attributes with useless data are given low importance in the distance function. © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.read less
- Liu, Jingjing, Eli T. Brown, and Remco Chang. "Find Distance Function, Hide Model Inference." 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) (October 2011). doi:10.1109/vast.2011.6102478.
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- 4j03d9555
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