Fast Sparse Representation Based on Smoothed ℓ0 Norm
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Publication:3608577
DOI10.1007/978-3-540-74494-8_49zbMath1173.94376OpenAlexW1995636681MaRDI QIDQ3608577
Massoud Babaie-Zadeh, G. Hosein Mohimani, Christian Jutten
Publication date: 5 March 2009
Published in: Independent Component Analysis and Signal Separation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-540-74494-8_49
Computational methods for sparse matrices (65F50) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items (12)
The non-convex sparse problem with nonnegative constraint for signal reconstruction ⋮ Faster maximum feasible subsystem solutions for dense constraint matrices ⋮ Heuristics for Finding Sparse Solutions of Linear Inequalities ⋮ Full spark frames ⋮ Matrix-wise \(\ell_0\)-constrained sparse nonnegative least squares ⋮ Compressed sensing of color images ⋮ Randomization of data acquisition and \(\ell_{1}\)-optimization (recognition with compression) ⋮ Sparse Component Analysis in Presence of Noise Using an Iterative EM-MAP Algorithm ⋮ DC Approximation Approach for ℓ0-minimization in Compressed Sensing ⋮ Compression and denoising using \(l _{0}\)-norm ⋮ Dictionary-Based Classification Models. Applications for Multichannel Neural Activity Analysis ⋮ Alternating direction method of multipliers for solving dictionary learning models
Cites Work
- Blind Source Separation by Sparse Decomposition in a Signal Dictionary
- Stable recovery of sparse overcomplete representations in the presence of noise
- Atomic Decomposition by Basis Pursuit
- Uncertainty principles and ideal atomic decomposition
- A generalized uncertainty principle and sparse representation in pairs of bases
- Analysis of Sparse Representation and Blind Source Separation
- Matching pursuits with time-frequency dictionaries
- Underdetermined blind source separation using sparse representations
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