Name
Abstract | ||
|---|---|---|
This paper extends the recently proposed and theoretically justified iterative thresholding and residual means (ITKrM) algorithm to learning dictionaries 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. Further experiments on image data confirm the importance of considering a low-rank component in the data and show that the algorithm compares favourably to its closest dictionary learning counterparts, wKSVD and BPFA, either in terms of computational complexity or in terms of consistency between the dictionaries learned from corrupted and uncorrupted data. To further confirm the appropriateness of the learned dictionaries, we explore an application to sparsity-based image inpainting. There the ITKrMM dictionaries show a similar performance to other learned dictionaries like wKSVD and BPFA and a superior performance to other algorithms based on pre-defined/analytic dictionaries. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s13634-018-0533-0 | EURASIP journal on advances in signal processing |
Keywords | Field | DocType |
Dictionary learning,Sparse coding,Sparse component analysis,Thresholding,K-means,Erasures,Masked data,Corrupted data,Inpainting | Training set,k-means clustering,Residual,Dictionary learning,Computer science,Neural coding,Inpainting,Artificial intelligence,Thresholding,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
2018 | 1 | 1687-6172 |
Citations | PageRank | References |
1 | 0.35 | 28 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| V Naumova | 1 | 30 | 5.06 |
| Karin Schnass | 2 | 229 | 26.43 |