Interactive Dimensionality Reduction for Comparative Analysis

• Published in: arXiv.org
• Main Authors: Fujiwara, Takanori, Wei, Xinhai, Zhao, Jian, Kwan-Liu, Ma
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 27.10.2021
• Subjects: Algorithms; Comparative analysis; Computer Science - Human-Computer Interaction; Computer Science - Learning; Datasets; Direct reduction; Discriminant analysis; Flexibility; Optimization; Optimization algorithms; Statistics - Machine Learning
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2106.15481

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.