Efficient dictionary learning with sparseness-enforcing projections

DOI10.1007/s11263-015-0799-8zbMath1398.94051arXiv1604.04767OpenAlexW1987382993MaRDI QIDQ1799978

Matthias Rapp, Markus Thom, Günther Palm

Publication date: 19 October 2018

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1604.04767

zbMATH Keywords

dictionary learningsparse codingsparse representationsexplicit sparseness constraintssparseness-enforcing projections

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (1)

Projections onto the intersection of a one-norm ball or sphere and a two-norm ball or sphere

Uses Software

• BLAS
• GSL

Cites Work

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• A mathematical introduction to compressive sensing
• Linear convergence of iterative soft-thresholding
• Natural image statistics. A probabilistic approach to early computational vision.
• Topographic Independent Component Analysis
• Why Simple Shrinkage Is Still Relevant for Redundant Representations?
• Dictionary Learning Algorithms for Sparse Representation
• Independent component analysis applied to feature extraction from colour and stereo images
• Recursive Least Squares Dictionary Learning Algorithm
• Dictionary Optimization for Block-Sparse Representations
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• De-noising by soft-thresholding
• Comparing Measures of Sparsity
• Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization
• Image Super-Resolution Via Sparse Representation
• Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
• Coupled Dictionary Training for Image Super-Resolution
• An updated set of basic linear algebra subprograms (BLAS)
• For most large underdetermined systems of linear equations the minimal 𝓁₁‐norm solution is also the sparsest solution
• Some Theorems on Matrix Differentiation with Special Reference to Kronecker Matrix Products
• A Simplex Method for Function Minimization
• Best approximation in inner product spaces

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