AuthorsV. Naumova and K. Schnass
TitleDictionary Learning from Incomplete Data
AfilliationScientific Computing
Project(s)FunDaHD: Function-driven Data Learning in High Dimension, Center for Biomedical Computing (SFF)
StatusPublished
Publication TypeJournal Article
Year of Publication2018
JournalEURASIP Journal on Advances in Signal Processing
Volume2018:12
Pagination1-21
Date Published02/2018
Publisher Springer
Abstract

This paper extends the recently proposed and theoretically justified iterative thresholding and K residual means algorithm ITKrM to learning dicionaries from incomplete/masked training data (ITKrMM). It further adapts the algorithm to the presence of a low rank component in the data and provides a strategy for recovering this low rank component again from incomplete data. Several synthetic experiments show the advantages of incorporating information about the corruption into the algorithm. Finally, image inpainting is considered as application example, which demonstrates the superior performance of ITKrMM in terms of speed and/or reconstruction quality compared to its closest dictionary learning counterpart.

URLhttp://rdcu.be/HD8p
DOI
Citation Key25009

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