Dictionary learning with structured noise

Recently, lots of dictionary learning methods have been proposed and successfully applied. However, many of them assume that the noise in data is drawn from Gaussian or Laplacian distribution and therefore they typically adopt the 2 or 1 norm to characterize these two kinds of noise, respectively. S...

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Main Authors: ZHOU, Pan, FANG, Cong, LIN, Zhouchen, ZHANG, Chao, CHANG, Y. Edward
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/9002
https://ink.library.smu.edu.sg/context/sis_research/article/10005/viewcontent/2017_Neucom_DL.pdf
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spelling sg-smu-ink.sis_research-100052024-07-25T08:17:23Z Dictionary learning with structured noise ZHOU, Pan FANG, Cong LIN, Zhouchen ZHANG, Chao CHANG, Y. Edward Recently, lots of dictionary learning methods have been proposed and successfully applied. However, many of them assume that the noise in data is drawn from Gaussian or Laplacian distribution and therefore they typically adopt the 2 or 1 norm to characterize these two kinds of noise, respectively. Since this assumption is inconsistent with the real cases, the performance of these methods is limited. In this paper, we propose a novel dictionary learning with structured noise (DLSN) method for handling noisy data. We decompose the original data into three parts: clean data, structured noise, and Gaussian noise, and then characterize them separately. We utilize the low-rank technique to preserve the inherent subspace structure of clean data. Instead of only using the predefined distribution to fit the real distribution of noise, we learn an adaptive dictionary to characterize structured noise and employ the 2 norm to depict Gaussian noise. Such a mechanism can characterize noise more precisely. We also prove that our proposed optimization method can converge to a critical point and the convergence rate is at least sublinear. Experimental results on the data clustering task demonstrate the effectiveness and robustness of our method. 2018-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9002 info:doi/10.1016/J.NEUCOM.2017.07.041 https://ink.library.smu.edu.sg/context/sis_research/article/10005/viewcontent/2017_Neucom_DL.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Dictionary learning Structured noise Low rank representation Sparse representation Artificial Intelligence and Robotics Graphics and Human Computer Interfaces
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Dictionary learning
Structured noise
Low rank representation
Sparse representation
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
spellingShingle Dictionary learning
Structured noise
Low rank representation
Sparse representation
Artificial Intelligence and Robotics
Graphics and Human Computer Interfaces
ZHOU, Pan
FANG, Cong
LIN, Zhouchen
ZHANG, Chao
CHANG, Y. Edward
Dictionary learning with structured noise
description Recently, lots of dictionary learning methods have been proposed and successfully applied. However, many of them assume that the noise in data is drawn from Gaussian or Laplacian distribution and therefore they typically adopt the 2 or 1 norm to characterize these two kinds of noise, respectively. Since this assumption is inconsistent with the real cases, the performance of these methods is limited. In this paper, we propose a novel dictionary learning with structured noise (DLSN) method for handling noisy data. We decompose the original data into three parts: clean data, structured noise, and Gaussian noise, and then characterize them separately. We utilize the low-rank technique to preserve the inherent subspace structure of clean data. Instead of only using the predefined distribution to fit the real distribution of noise, we learn an adaptive dictionary to characterize structured noise and employ the 2 norm to depict Gaussian noise. Such a mechanism can characterize noise more precisely. We also prove that our proposed optimization method can converge to a critical point and the convergence rate is at least sublinear. Experimental results on the data clustering task demonstrate the effectiveness and robustness of our method.
format text
author ZHOU, Pan
FANG, Cong
LIN, Zhouchen
ZHANG, Chao
CHANG, Y. Edward
author_facet ZHOU, Pan
FANG, Cong
LIN, Zhouchen
ZHANG, Chao
CHANG, Y. Edward
author_sort ZHOU, Pan
title Dictionary learning with structured noise
title_short Dictionary learning with structured noise
title_full Dictionary learning with structured noise
title_fullStr Dictionary learning with structured noise
title_full_unstemmed Dictionary learning with structured noise
title_sort dictionary learning with structured noise
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/9002
https://ink.library.smu.edu.sg/context/sis_research/article/10005/viewcontent/2017_Neucom_DL.pdf
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