FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns
FacIt: Factorizing Tensors into Interpretable and Scrutinizable Patterns
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DOI：
10.1109/visual.2019.8933750
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Xidao Wen;K. Pelechrinis;Y. Lin;Xi Liu;Yongsu Ahn;Nan Cao
中科院分区：
文献类型：
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作者：
Xidao Wen;K. Pelechrinis;Y. Lin;Xi Liu;Yongsu Ahn;Nan Cao
张量分解被广泛用于从多维数据中发现潜在模式。然而，解释或仔细检查张量分解结果绝非易事。我们介绍FacIt，一个通用的可视化分析系统，它直接将张量格式的数据分解为模式的可视化表示，以方便结果解释，审查，信息查询以及模型选择。我们的设计包括(i)一套模型审查和检查工具，允许有效的张量模型选择（通常称为秩选择问题）和（ii）交互式可视化设计，使用户能够进行特征和内容驱动的模式发现。我们通过带有策略采用分析的使用场景来演示系统的有效性。
Tensor Factorization has been widely used in many fields to discover latent patterns from multidimensional data. Interpreting or scrutinizing the tensor factorization results are, however, by no means easy. We introduce FacIt, a generic visual analytic system that directly factorizes tensor-formatted data into a visual representation of patterns to facilitate result interpretation, scrutinization, information query, as well as model selection. Our design consists of (i) a suite of model scrutinizing and inspection tools that allows efficient tensor model selection (commonly known as rank selection problem) and (ii) an interactive visualization design that empowers users with both characteristics- and content-driven pattern discovery. We demonstrate the effectiveness of our system through usage scenarios with policy adoption analysis.