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Parametric dictionary learning using steepest descent.
| Content Provider | CiteSeerX |
|---|---|
| Author | Ataee, Mahdi Zayyani, Hadi Babaie-Zadeh, Massoud Jutten, Christian |
| Abstract | 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. Index Terms — Dictionary learning, Sparse representation, parametric dictionary, Sparse Component Analysis. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Sparse Representation Parametric Dictionary Noisy Speech Signal Steepest Descent Algorithm Atom Function Simulation Result Mean Square Error Steepest Descent Direction Gabor Function Atom Structure Gabor-like Atom Sparse Component Analysis Sparse Representation Step Index Term Dictionary Learning Descent Direction Thresholded Version |
| Content Type | Text |
