Iterative Hard Thresholding and L0 Regularisation

Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Ročník 3; s. III-877 - III-880
Hlavní autoři: Blumensath, T., Yaghoobi, M., Davies, M.E.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2007
Témata:
Approximation algorithms
Convergence
Cost function
Equations
Iterative algorithms
Iterative Thresholding
L0 Regularisation
Matching pursuit algorithms
Noise reduction
Pursuit algorithms
Signal processing
Signal processing algorithms
Sparse Approximations
ISBN:9781424407279, 1424407273
ISSN:1520-6149
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation cost function, which measures both, the reconstruction error and the number of elements in the representation. Simulation results suggest that the algorithm is comparable in performance to a commonly used alternative method.
ISBN:9781424407279
1424407273
ISSN:1520-6149
DOI:10.1109/ICASSP.2007.366820