title: Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction.
abstract: 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.
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Chao
authors.initials: C
authors.lastname: Han
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Leanna
authors.initials: L
authors.lastname: House
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA, United States of America.
authors.forename: Scotland C
authors.initials: SC
authors.lastname: Leman
mesh_terms: D003196:Computer Graphics; D017711:Nonlinear Dynamics; D013223:Statistics as Topic / Q000379:methods*