NDLI: Parametric dictionary learning using steepest descent

Content Provider	IEEE Xplore Digital Library
Author	Ataee, M. Zayyani, H. Babaie-Zadeh, M. Jutten, C.
Copyright Year	2010
Description	Author affiliation: Department Of Electrical Engineering, Sharif University of Technology, Tehran, Iran (Ataee, M.; Zayyani, H.; Babaie-Zadeh, M.) \|\| GIPSA-Lab, Grenoble, and Institut Universitaire de France, France (Jutten, C.)
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 $Smoothed-ℓ^{0}$ (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.
Starting Page	1978
Ending Page	1981
File Size	131248
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495278
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Signal design Learning systems Signal analysis Signal processing algorithms Compressed sensing Speech analysis Discrete cosine transforms Sparse matrices Mean square error methods Sparse Component Analysis Dictionary learning Sparse representation parametric dictionary
Content Type	Text
Resource Type	Article

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