2021/02/20 by Kris Sankaran, Sankaran, Kris
Computer Science · #Computation (stat.CO) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Retrieval and Classification Techniques #Machine Learning and Data Classification
paper · pdf · doi:10.48550/arxiv.2102.10388
openalex publication_date 2021/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many modern datasets don't fit neatly into n × p matrices, but most techniques for measuring statistical stability expect rectangular data. We study methods for stability assessment on non-rectangular data, using statistical learning algorithms to extract rectangular latent features. We design controlled simulations to characterize the power and practicality of competing approaches. This motivates new strategies for visualizing feature stability. Our stability curves supplement the direct analysis, providing information about the reliability of inferences based on learned features. Finally, we illustrate our approach using a spatial proteomics dataset, where machine learning tools can augment the scientist's workflow, but where guarantees of statistical reproducibility are still central. Our raw data, packaged code, and experimental outputs are publicly available.