Han Chao, House Leanna, Leman Scotland C
Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America.
PLoS One. 2016 Feb 23;11(2):e0129122. doi: 10.1371/journal.pone.0129122. eCollection 2016.
Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.
生成地形映射（GTM）由毕晓普等人于1996年提出，是一种用于可视化高维数据的强大非线性潜在变量建模方法。当典型的线性方法失效时，它已显示出其有用性。然而，GTM仍然存在缺点。其复杂的数据参数化使得GTM难以拟合且对模型中的微小变化敏感。因此，我们将GTM扩展为一个可视化分析框架，以便用户可以指导参数化并从多个GTM视角评估数据。具体而言，我们利用GTM可视化开发了针对数据的视觉到参数交互（V2PI）的理论和方法。结果是一个促进数据探索的动态版GTM。我们将新版本称为V2PI-GTM。在本文中，我们分阶段开发V2PI-GTM，并在文本挖掘案例研究的背景下展示其优势。
