A theoretical guarantee for data completion via geometric separation
Functional Analysis
2017-05-31 v1
Abstract
Scientific and commercial data is often incomplete. Recovery of the missing information is an important pre-processing step in data analysis. Real-world data can in many cases be represented as a superposition of two or more different types of structures. For example, images may often be decomposed into texture and cartoon-like components. When incomplete data comes from a distribution well-represented as a mixture of different structures, a sparsity-based method combining concepts from data completion and data separation can successfully recover the missing data. This short note presents a theoretical guarantee for success of the combined separation and completion approach which generalizes proofs from the distinct problems.
Cite
@article{arxiv.1705.10745,
title = {A theoretical guarantee for data completion via geometric separation},
author = {Emily J. King and James M. Murphy},
journal= {arXiv preprint arXiv:1705.10745},
year = {2017}
}