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. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Proceedings in Applied Mathematics & Mechanics – Wiley
Published: Jan 1, 2017
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