Observation-level interaction with statistical models for visual analytics

Abstract

In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus "observation") in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.KEYWORDS: observation-level interaction, visual analytics, statistical models. INDEX TERMS: H.5.0 [Human-Computer Interaction] INTRODUCTIONVisual analytics is "the science of analytical reasoning facilitated by interactive visual interfaces" [1]. The goal of visual analytics (VA) is to extract information, perform exploratory analyses, and validate hypotheses through an interactive exploration process known as sensemaking [2]. In this sensemaking loop, users proceed through a complex combination of proposing and evaluating hypotheses and schemas about their data, with the ultimate goal of gaining insight (i.e. "making sense of" the data). A wide variety of statistical models have been specifically designed for visualizations of this purpose. Thus, many visual analytic systems are fundamentally based on interaction with statistical models and algorithms, using visualization as the medium for the communication (i.e. where the interaction occurs). This communication is performed via direct interaction with the parameters of the model. For example, Interactive Principal Component Analysis, iPCA [3], allows the user to change the weight for each dimension in calculating the direction of projection using multiple sliders (one slider per dimension). Also, in an interactive visualization using MDS [4], the user can weight the dissimilarities in the calculation of the stress function through similar visual controls.In both instances, the model is made aware of the user input through a formal and direct modification of a parameter (i.e. parameter level interacti...

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