DocumentCode :
68955
Title :
A Proximal Method for Dictionary Updating in Sparse Representations
Author :
Guan-Ju Peng ; Wen-Liang Hwang
Author_Institution :
Inst. of Inf. Sci., Taipei, Taiwan
Volume :
63
Issue :
15
fYear :
2015
fDate :
Aug.1, 2015
Firstpage :
3946
Lastpage :
3958
Abstract :
In this paper, we propose a new dictionary updating method for sparse dictionary learning. Our method imposes the ℓ0 norm constraint on coefficients as well as a proximity regularization on the distance of dictionary modifications in the dictionary updating process. We show that the derived dictionary updating rule is a generalization of the K-SVD method. We study the convergence and the complexity of the proposed method. We also compare its performance with that of other methods.
Keywords :
approximation theory; computational complexity; learning (artificial intelligence); signal processing; singular value decomposition; K-SVD method; dictionary modifications; dictionary updating process; proximal method; proximity regularization; sparse dictionary learning; sparse representations; Approximation methods; Convergence; Dictionaries; Encoding; Estimation; Optimization; Signal processing algorithms; Dictionary learning; supervised learning;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2015.2434323
Filename :
7109931
Link To Document :
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