为科学发现设计可复现的无监督学习流程
Unsupervised Machine Learning for Scientific Discovery: Workflow and Best Practices
- 构建从问题定义到结果验证的标准化无监督学习流程
- 通过天文星团化学组成分析验证流程有效性
- 适合希望提升科研可复现性的跨学科研究者
无监督机器学习广泛应用于气候科学、生物医学、天文学、化学等关键领域的大规模无标签数据挖掘，以实现数据驱动的科学发现。然而，尽管应用广泛，当前缺乏标准化的无监督学习工作流程，难以保证科学发现的可靠性与可复现性。本文提出一个结构化的工作流程，涵盖可验证科学问题的提出、稳健的数据准备与探索、多种建模技术的应用、通过评估结论稳定性与泛化能力进行严格验证，以及结果的有效沟通与文档化，以确保可复现的科学发现。为展示该流程，我们以银河系球状星团基于化学成分的再分类为例开展案例研究，凸显验证的重要性，并说明精心设计的工作流程如何推动科学进步。
原文摘要 · Abstract (English)
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread utilization, there is a lack of standardization in unsupervised learning workflows for making reliable and reproducible scientific discoveries. In this paper, we present a structured workflow for using unsupervised learning techniques in science. We highlight and discuss best practices starting with formulating validatable scientific questions, conducting robust data preparation and exploration, using a range of modeling techniques, performing rigorous validation by evaluating the stability and generalizability of unsupervised learning conclusions, and promoting effective communication and documentation of results to ensure reproducible scientific discoveries. To illustrate our proposed workflow, we present a case study from astronomy, seeking to refine globular clusters of Milky Way stars based upon their chemical composition. Our case study highlights the importance of validation and illustrates how the benefits of a carefully-designed workflow for unsupervised learning can advance scientific discovery.
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