Interactive Dimensionality Reduction for Comparative Analysis

Takanori Fujiwara, Xinhai Wei, Jian Zhao, Kwan-Liu Ma

View presentation:2021-10-27T14:15:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
The analyst is analyzing the Wine dataset with our interactive dimensionality reduction (DR) framework in the Jupyter Notebook. The result produced with a new DR method, Unified Linear Comparative Analysis (ULCA), is visualized with the UI. (a) shows interactively adjustable parameters. (b) depicts a lower-dimensional representation produced by ULCA. The analyst can directly manipulate the centroid or scatteredness of each group by moving or scaling the corresponding confidence ellipse to trigger the backward parameter selection. (c) informs a numerical mapping from the original attributes to each component. With (d), the analyst can save the current state of visualizations and parameters.

Finding the similarities and differences between two or more groups of datasets is a fundamental analysis task. For high-dimensional data, dimensionality reduction (DR) methods are often used to find the characteristics of each group. However, existing DR methods provide limited capability and flexibility for such comparative analysis as each method is designed only for a narrow analysis target, such as identifying factors that most differentiate groups. In this work, we introduce an interactive DR framework where we integrate our new DR method, called ULCA (unified linear comparative analysis), with an interactive visual interface. ULCA unifies two DR schemes, discriminant analysis and contrastive learning, to support various comparative analysis tasks. To provide flexibility for comparative analysis, we develop an optimization algorithm that enables analysts to interactively refine ULCA results. Additionally, we provide an interactive visualization interface to examine ULCA results with a rich set of analysis libraries. We evaluate ULCA and the optimization algorithm to show their efficiency as well as present multiple case studies using real-world datasets to demonstrate the usefulness of our framework.