Observation-Level and Parametric Interaction for High-Dimensional Data Analysis
Observation-Level and Parametric Interaction for High-Dimensional Data Analysis
复制标题
高维数据分析的观测级和参数交互
DOI：
10.1145/3158230
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Chris North
中科院分区：
文献类型：
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作者：
J. Self;Michelle Dowling;John E. Wenskovitch;Ian C Crandell;Ming Wang;L. House;Scotland Leman;Chris North
探索高维数据具有挑战性。降维算法（如加权多维缩放）通过将数据集投影到二维以进行可视化来支持数据探索。这些投影可以通过参数交互、调整底层参数化和观测级交互来探索，直接与投影内的点交互。在这篇文章中，我们提出了一个受控的可用性研究的结果，确定参数交互，观察级交互，以及它们的组合之间的差异，优点和缺点。本研究评估了两种交互技术对非统计算法专家进行的特定领域高维数据分析的影响。这项研究是使用Andromeda进行的，Andromeda是一种能够进行参数和观察级交互以提供深入数据探索的工具。结果表明，这两种形式的相互作用服务于不同的，但互补的，通过可控降维算法获得洞察力的目的。
Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multidimensional scaling, support data exploration by projecting datasets to two dimensions for visualization. These projections can be explored through parametric interaction, tweaking underlying parameterizations, and observation-level interaction, directly interacting with the points within the projection. In this article, we present the results of a controlled usability study determining the differences, advantages, and drawbacks among parametric interaction, observation-level interaction, and their combination. The study assesses both interaction technique effects on domain-specific high-dimensional data analyses performed by non-experts of statistical algorithms. This study is performed using Andromeda, a tool that enables both parametric and observation-level interaction to provide in-depth data exploration. The results indicate that the two forms of interaction serve different, but complementary, purposes in gaining insight through steerable dimension reduction algorithms.