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Parametric dictionary learning using steepest descent

Ataee, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2010.5495278
  3. Publisher: 2010
  4. Abstract:
  5. In this paper, we suggest to use a steepest descent algorithm for learning a parametric dictionary in which the structure or atom functions are known in advance. The structure of the atoms allows us to find a steepest descent direction of parameters instead of the steepest descent direction of the dictionary itself. We also use a thresholded version of Smoothed- ℓ0 (SL0) algorithm for sparse representation step in our proposed method. Our simulation results show that using atom structure similar to the Gabor functions and learning the parameters of these Gabor-like atoms yield better representations of our noisy speech signal than non parametric dictionary learning methods like K-SVD, in terms of mean square error of sparse representations
  6. Keywords:
  7. Atom structure ; Dictionary learning ; Gabor function ; Noisy speech signals ; Non-parametric ; Parametric dictionary ; Simulation result ; Sparse component analysis ; Sparse representation ; Steepest descent ; Steepest descent algorithm ; Signal processing ; Atoms
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 14 March 2010 through 19 March 2010 ; March , 2010 , Pages 1978-1981 ; 15206149 (ISSN) ; 9781424442966 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5495278