When
Where
Speaker: Takanori Fujiwara, Computer Science, UA
Title: Communicating High-dimensional Data through Dimensionality Reduction and Interactive Visualizations
Abstract: High-dimensional data contains a rich set of measured observations of a phenomenon and is ubiquitous for data analysis. However, its high dimensionality makes data analysis challenging. One promising approach to address this challenge is to extract essential information by applying dimensionality reduction (DR) and then visualize the result for further data analysis. In this talk, I will discuss three aspects necessary to advance this analytical approach: (1) develop interactive DR methods, (2) investigate the reliability of DR results, and (3) design effective visualizations for DR results. Specifically, I will introduce new DR algorithms that enable interactive comparison of data groups and reveal the distinctive characteristics of each group. Second, I will demonstrate how existing DR algorithms can potentially hide prominent data patterns and lead to biased analytical insights, and I will present new algorithms designed to mitigate such potential biases. Lastly, I will showcase planetarium-scale, 3D immersive visualizations that communicate DR results in an engaging and intuitive way. Advancing these three aspects will enable a more effective, trustworthy, and intuitive way of communicating and deriving insights from high-dimensional data.