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.

翻译：可视化分析如今在跨学科决策中发挥着核心作用，但其结果可能并不可靠：分析所得的知识或洞见未必能准确反映底层数据。本论文聚焦于降维技术，致力于提升可视化分析的可靠性。降维技术通过将高维数据降至二维或三维以实现可视化分析，然而其固有误差可能损害可视化分析的可靠性。为此，本研究系统探讨了从业者运用降维技术进行可视化分析时面临的可靠性挑战，进而提出涵盖新型评估指标、优化策略与交互技术在内的系统性解决方案。最后，本文通过阐述研究成果如何为构建更可靠的可视化分析实践奠定理论基础，完成对全篇研究的总结。