揭示降维会误导可视化分析，提出改进方法提升可靠性
Dimensionality Reduction Considered Harmful (Some of the Time)
- 分析降维导致的可视化偏差，识别其不可靠根源
- 设计新评估指标与优化策略，减少降维过程中的信息损失
- 适合数据可视化、科学计算等需高可靠性的研究者
视觉分析在多个领域决策中扮演核心角色，但其结果可能不可靠：所获得的知识或洞察未必真实反映底层数据。本文聚焦于降维（DR）对视觉分析可靠性的影响。虽然降维技术能将高维数据投影至二维或三维以实现可视化，但其固有的误差可能损害分析的可信度。为此，本文系统研究了从业者在使用降维进行视觉分析时面临的关键可靠性挑战，并提出了相应的技术解决方案，包括新的评估指标、优化策略和交互方法。最后，论文总结指出，这些贡献为实现更可靠的视觉分析实践奠定了基础。
原文摘要 · Abstract (English)
Visual analytics now plays a central role in decision-making across diverse disciplines, but it can be unreliable: the knowledge or insights derived from the analysis may not accurately reflect the underlying data. In this dissertation, we improve the reliability of visual analytics with a focus on dimensionality reduction (DR). DR techniques enable visual analysis of high-dimensional data by reducing it to two or three dimensions, but they inherently introduce errors that can compromise the reliability of visual analytics. To this end, I investigate reliability challenges that practitioners face when using DR for visual analytics. Then, I propose technical solutions to address these challenges, including new evaluation metrics, optimization strategies, and interaction techniques. We conclude the thesis by discussing how our contributions lay the foundation for achieving more reliable visual analytics practices.
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