On the identifiability of overcomplete dictionaries via the minimisation principle underlying K-SVD

DOI10.1016/j.acha.2014.01.005zbMath1297.94018arXiv1301.3375OpenAlexW2034683677MaRDI QIDQ741264

Karin Schnass

Publication date: 11 September 2014

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1301.3375

zbMATH Keywords

sparse representationdictionary learningsparse codingsparse component analysissampling complexityK-SVDdictionary identificationfinite sample sizeminimisation criterion

Mathematics Subject Classification ID

Factorization of matrices (15A23) Learning and adaptive systems in artificial intelligence (68T05) Sampling theory in information and communication theory (94A20) Source coding (94A29)

Related Items

Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization ⋮ Unnamed Item ⋮ Fast overcomplete dictionary construction with probabilistic guarantees ⋮ Learning semidefinite regularizers ⋮ Convergence radius and sample complexity of ITKM algorithms for dictionary learning ⋮ Unnamed Item ⋮ Customized dictionary learning for subdatasets with fine granularity ⋮ An efficient algorithm for overcomplete sparsifying transform learning with signal denoising ⋮ On the identifiability of overcomplete dictionaries via the minimisation principle underlying K-SVD ⋮ Compressed dictionary learning ⋮ Compressive sensing and neural networks from a statistical learning perspective ⋮ Multilinear Compressive Sensing and an Application to Convolutional Linear Networks

Uses Software

• PDCO
• CoSaMP

Cites Work

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