用无监督学习自动找出4D-STEM数据中晶体取向的最优聚类数。
Unsupervised Multi-Clustering and Decision-Making Strategies for 4D-STEM Orientation Mapping
- 基于NMF和损失函数选择最佳聚类数量
- 结合图像质量评估提升结果稳定性和准确性
- 适合材料科学中多维结构分析的研究者
本研究提出一种将无监督学习与决策策略融合的新方法，用于4D-STEM数据的高级分析，以非负矩阵分解（NMF）为主要聚类手段。通过引入K-Component Loss方法和图像质量评估（IQA）指标，系统性地确定实现鲁棒且可解释取向映射所需的最优成分数（k），有效平衡重构保真度与模型复杂度。同时强调数据预处理对提升聚类稳定性与准确性的关键作用。空间权重矩阵分析结合阈值可视化，揭示了数据集中重叠区域的特征，有助于理解不同聚类间的相互作用。结果表明，NMF结合先进IQA指标与预处理技术，在4D-STEM数据的可靠取向映射与结构分析中具有显著潜力，为多维材料表征的未来应用提供支持。
原文摘要 · Abstract (English)
This study presents a novel integration of unsupervised learning and decision-making strategies for the advanced analysis of 4D-STEM datasets, with a focus on non-negative matrix factorization (NMF) as the primary clustering method. Our approach introduces a systematic framework to determine the optimal number of components (k) required for robust and interpretable orientation mapping. By leveraging the K-Component Loss method and Image Quality Assessment (IQA) metrics, we effectively balance reconstruction fidelity and model complexity. Additionally, we highlight the critical role of dataset preprocessing in improving clustering stability and accuracy. Furthermore, our spatial weight matrix analysis provides insights into overlapping regions within the dataset by employing threshold-based visualization, facilitating a detailed understanding of cluster interactions. The results demonstrate the potential of combining NMF with advanced IQA metrics and preprocessing techniques for reliable orientation mapping and structural analysis in 4D-STEM datasets, paving the way for future applications in multi-dimensional material characterization.
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