NDLI: Non-negative Dictionary-Learning Algorithm for the Analysis Model Based on L1 Norm

Content Provider	IEEE Xplore Digital Library
Author	Yujie Li Shuxue Ding Zhenni Li Wuhui Chen
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of Aizu, Aizu-Wakamatsu, Japan (Yujie Li; Shuxue Ding; Zhenni Li; Wuhui Chen)
Abstract	Sparse representation of signals has been successfully applied in signal processing. Most of existing methods for sparse representation are based on the synthesis model, in which the dictionary is over complete. This paper addresses the dictionary learning and sparse representation with the so-called analysis model. Based on this model, the analysis dictionary multiplying the signals can lead to a sparse outcome. Though this model has been studied in some literatures, there are still less investigations in the context of nonnegative dictionary learning for signal representation. So we focus on nonnegative dictionary learning for signal representation. In this paper, we propose to learn an analysis dictionary from signals using $l_{1}-norm$ as the sparsity measure. In the formulation, we adopt the Euclidean distance as the error measure. Based on these, we present a new algorithm for the nonnegative dictionary learning and sparse representation for signals. Numerical experiments on recovery of analysis dictionary in the noiseless and noisy situation show the effectiveness of the proposed method.
Sponsorship	IIAI
Starting Page	495
Ending Page	499
File Size	425141
Page Count	5
File Format	PDF
ISBN	9781479999576
e-ISBN	9781479999583
DOI	10.1109/IIAI-AAI.2015.183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Analytical models Dictionaries Computational modeling ?1-norm Analysis model Analysis dictionary learning Nonnegative Cost function Sparse representation Sparse matrices Noise measurement
Content Type	Text
Resource Type	Article

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