# Unsupervised Multi-Clustering and Decision-Making Strategies for 4D-STEM Orientation Mapping

arXiv: https://arxiv.org/abs/2503.06699 · HTML: https://mlsanity.com/paper/2503.06699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 62 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator](https://mlsanity.com/paper/1608.07433/) (2016)
- [Phase Object Reconstruction for 4D-STEM using Deep Learning](https://mlsanity.com/paper/2202.12611/) (2022)
- [Improved ACOM pattern matching in 4D STEM through adaptive sub pixel peak detection and image reconstruction](https://mlsanity.com/paper/2305.02124/) (2023)

## Abstract (arXiv)

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.