Work

One-Way Matching of Datasets with Low Rank Signals

Year: 2022

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2204.13858

Authors: Chen, Shuxiao, Jiang, Sizun, Ma, Zongming, Nolan, Garry P., Zhu, Bokai

Institutions: University of Pennsylvania, Beth Israel Deaconess Medical Center, Stanford University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Bill and Melinda Gates Foundation

Awards: Matching and Data Integration, Oregon Health & Science University - to discover circulating markers to determine the size of the HIV reservoir in the body, to clinically test interventions that might reduce the size...

OpenAlex ID: W4225312045

Cites:

Cited by: 9

Related to: 10