为降低维可视化分析风险，提供新手入门的论文阅读指南。
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
- 基于已有文献分类，构建分阶阅读路径
- 帮助用户评估自身降维能力并定位进阶论文
- 经专家访谈验证，实用性强且覆盖全面
使用降维（DR）进行可视化分析可能因数据表示固有失真而不可靠。为此，学界提出了大量方法以提升基于降维的可视化分析可靠性。然而，文献数量庞大且类型多样，使初学者难以确定从何处入手。为此，本文提出一套论文阅读指南，帮助使用者（1）评估当前降维技能水平，（2）识别可深化理解的相关研究。基于三位降维与数据可视化领域专家的访谈，验证了该指南在意义、全面性与实用性上的价值。
原文摘要 · Abstract (English)
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual analytics with DR. Relying on the previous classification of the relevant literature, our guide helps both practitioners to (1) assess their current DR expertise and (2) identify papers that will further enhance their understanding. Interview studies with three experts in DR and data visualizations validate the significance, comprehensiveness, and usefulness of our guide.
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