Parametric dictionary learning using steepest descent

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 Smoothed0 (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.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Mahdi Ataee, Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten
• Keywords: ICASSP 2010, Signal Processing, Sparse Representations, Steepest Descent, Steepest Descent Direction