LoVis: Local Pattern Visualization for Model Refinement

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dc.contributor.authorZhao, Kaiyuen_US
dc.contributor.authorWard, Matthew O.en_US
dc.contributor.authorRundensteiner, Elke A.en_US
dc.contributor.authorHiggins, Huong N.en_US
dc.contributor.editorH. Carr, P. Rheingans, and H. Schumannen_US
dc.date.accessioned2015-03-03T12:35:36Z
dc.date.available2015-03-03T12:35:36Z
dc.date.issued2014en_US
dc.description.abstractLinear models are commonly used to identify trends in data. While it is an easy task to build linear models using pre-selected variables, it is challenging to select the best variables from a large number of alternatives. Most metrics for selecting variables are global in nature, and thus not useful for identifying local patterns. In this work, we present an integrated framework with visual representations that allows the user to incrementally build and verify models in three model spaces that support local pattern discovery and summarization: model complementarity, model diversity, and model representivity. Visual representations are designed and implemented for each of the model spaces. Our visualizations enable the discovery of complementary variables, i.e., those that perform well in modeling different subsets of data points. They also support the isolation of local models based on a diversity measure. Furthermore, the system integrates a hierarchical representation to identify the outlier local trends and the local trends that share similar directions in the model space. A case study on financial risk analysis is discussed, followed by a user study.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12389en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12389en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleLoVis: Local Pattern Visualization for Model Refinementen_US
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33-Issue 3
EuroVis14: Eurographics Conference on Visualization