2022/04/29 by Shuxiao Chen, Sizun Jiang, Chen, Shuxiao +7
Computer Science · Engineering · #Error Correcting Code Techniques #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Quantitative Methods (q-bio.QM) #Sparse and Compressive Sensing Techniques #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.2204.13858
openalex publication_date 2022/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study one-way matching of a pair of datasets with low rank signals. Under a stylized model, we first derive information-theoretic limits of matching under a mismatch proportion loss. We then show that linear assignment with projected data achieves fast rates of convergence and sometimes even minimax rate optimality for this task. The theoretical error bounds are corroborated by simulated examples. Furthermore, we illustrate practical use of the matching procedure on two single-cell data examples.