Perturbations of Compressed Data Separation With Redundant Tight Frames

In the era of big data, the multi-modal data can be seen everywhere. Research on such data has attracted extensive attention in the past few years. In this paper, we investigate the perturbations of compressed data separation with redundant tight frames via Φ̃-Iq-minimization....

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Main Authors: Feng Zhang, Jianjun Wang, Yao Wang, Jianwen Huang, Wendong Wang
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8398197/
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spelling doaj-b742c505b39244fb8d867454c385b1d12021-03-29T21:03:05ZengIEEEIEEE Access2169-35362018-01-016358443585610.1109/ACCESS.2018.28510198398197Perturbations of Compressed Data Separation With Redundant Tight FramesFeng Zhang0https://orcid.org/0000-0003-1000-8877Jianjun Wang1Yao Wang2Jianwen Huang3https://orcid.org/0000-0003-3792-1383Wendong Wang4School of Mathematics and Statistics, Southwest University, Chongqing, ChinaSchool of Mathematics and Statistics, Southwest University, Chongqing, ChinaSchool of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, ChinaSchool of Mathematics and Statistics, Southwest University, Chongqing, ChinaSchool of Mathematics and Statistics, Southwest University, Chongqing, ChinaIn the era of big data, the multi-modal data can be seen everywhere. Research on such data has attracted extensive attention in the past few years. In this paper, we investigate the perturbations of compressed data separation with redundant tight frames via Φ̃-Iq-minimization. By exploiting the properties of the redundant tight frame and the perturbation matrix, i.e., mutual coherence, null space property, and restricted isometry property, the condition on reconstruction of sparse signal with redundant tight frames is established, and the error estimation between the local optimal solution and the original signal is also provided. Numerical experiments are carried out to show that Φ̃-Iq-minimization is robust and stable for the reconstruction of sparse signal with redundant tight frames. To our knowledge, our works may be the first study concerning the perturbations of the measurement matrix and the redundant tight frame for compressedhttps://ieeexplore.ieee.org/document/8398197/Compressed data separationperturbationnull space propertyrestricted isometry property
collection DOAJ
language English
format Article
sources DOAJ
author Feng Zhang
Jianjun Wang
Yao Wang
Jianwen Huang
Wendong Wang
spellingShingle Feng Zhang
Jianjun Wang
Yao Wang
Jianwen Huang
Wendong Wang
Perturbations of Compressed Data Separation With Redundant Tight Frames
IEEE Access
Compressed data separation
perturbation
null space property
restricted isometry property
author_facet Feng Zhang
Jianjun Wang
Yao Wang
Jianwen Huang
Wendong Wang
author_sort Feng Zhang
title Perturbations of Compressed Data Separation With Redundant Tight Frames
title_short Perturbations of Compressed Data Separation With Redundant Tight Frames
title_full Perturbations of Compressed Data Separation With Redundant Tight Frames
title_fullStr Perturbations of Compressed Data Separation With Redundant Tight Frames
title_full_unstemmed Perturbations of Compressed Data Separation With Redundant Tight Frames
title_sort perturbations of compressed data separation with redundant tight frames
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2018-01-01
description In the era of big data, the multi-modal data can be seen everywhere. Research on such data has attracted extensive attention in the past few years. In this paper, we investigate the perturbations of compressed data separation with redundant tight frames via Φ̃-Iq-minimization. By exploiting the properties of the redundant tight frame and the perturbation matrix, i.e., mutual coherence, null space property, and restricted isometry property, the condition on reconstruction of sparse signal with redundant tight frames is established, and the error estimation between the local optimal solution and the original signal is also provided. Numerical experiments are carried out to show that Φ̃-Iq-minimization is robust and stable for the reconstruction of sparse signal with redundant tight frames. To our knowledge, our works may be the first study concerning the perturbations of the measurement matrix and the redundant tight frame for compressed
topic Compressed data separation
perturbation
null space property
restricted isometry property
url https://ieeexplore.ieee.org/document/8398197/
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