Interactive Dimensionality Reduction for Comparative Analysis
This work addresses the problem of comparative analysis for analysts dealing with high-dimensional data, offering an incremental improvement by integrating existing techniques into a flexible framework.
The paper tackles the limited capability of existing dimensionality reduction methods for comparative analysis of high-dimensional datasets by introducing an interactive framework with a new method called ULCA, which unifies discriminant analysis and contrastive learning, and includes an optimization algorithm and visual interface for refinement, demonstrated through case studies with real-world datasets.
Finding the similarities and differences between 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. This paper presents 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, the interactive visualization interface facilitates interpretation and refinement of the ULCA results. 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 this framework.