Projects

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA . In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data. For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components. We provide multiple examples of our technique using real-world datasets. As a special case, we show how to propagate multivariate normal distributions through PCA in closed form. Furthermore, we discuss extensions and limitations of our approach.

Comparing data distributions is a core focus in descriptive statistics, and part of most data analysis processes across disci-plines. In particular, comparing distributions entails numerous tasks, ranging from identifying global distribution properties,comparing aggregated statistics (e.g., mean values), to the local inspection of single cases. While various specialized visualiza-tions have been proposed (e.g., box plots, histograms, or violin plots), they are not usually designed to support more than a fewtasks, unless they are combined. In this paper, we present the v-plot designer; a technique for authoring custom hybrid charts,combining mirrored bar charts, difference encodings, and violin-style plots. v-plots are customizable and enable the simulta-neous comparison of data distributions on global, local, and aggregation levels. Our system design is grounded in an expertsurvey that compares and evaluates 20 common visualization techniques to derive guidelines for the task-driven selection ofappropriate visualizations. This knowledge externalization step allowed us to develop a guiding wizard that can tailor v-plotsto individual tasks and particular distribution properties. Finally, we confirm the usefulness of our system design and the user-guiding process by measuring the fitness for purpose and applicability in a second study with four domain and statistic expert

We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models. Our framework combines an iterative XAI pipeline with eight global monitoring and steering mechanisms, including quality monitoring, provenance tracking, model comparison, and trust building. To operationalize the framework, we present explAIner, a visual analytics system for interactive and explainable machine learning that instantiates all phases of the suggested pipeline within the commonly used TensorBoard environment. We performed a user-study with nine participants across different expertise levels to examine their perception of our workflow and to collect suggestions to fill the gap between our system and framework. The evaluation confirms that our tightly integrated system leads to an informed machine learning process while disclosing opportunities for further extensions.