Parametric Dictionary Learning Using Steepest Descent

Mahdi Ataee (1) , Hadi Zayyani (1) , Massoud Babaie-Zadeh (1) , Christian Jutten (2)

1 EE department of SUT [Tehran] - Department of Electrical Engineering [Tehran]
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

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.

Mots clés: Dictionary learning; Sparse representation; parametric dictionary; Sparse Component Analysis

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Mahdi Ataee, Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten. Parametric Dictionary Learning Using Steepest Descent. ICASSP 2010 - IEEE International Conference on Acoustics, Speech and Signal Processing, Mar 2010, Dallas, United States. pp.1978-1981. ⟨hal-00466282⟩