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Adaptive Signal Processing

Feature LMS Algorithms


In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity. This work proposes a set of LMS-type algorithms, collectively called Feature LMS (F-LMS) algorithms, setting forth a hidden feature of the unknown parameters, which ultimately would improve convergence speed and steady-state mean-squared error.

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Authors:
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, Markus Vinicius Santos Lima
Submitted On:
14 April 2018 - 11:53pm
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ICASSP2018_Diniz_Presentation.pdf

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[1] Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, Markus Vinicius Santos Lima, "Feature LMS Algorithms", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2874. Accessed: May. 26, 2019.
@article{2874-18,
url = {http://sigport.org/2874},
author = {Paulo Sergio Ramirez Diniz; Hamed Yazdanpanah; Markus Vinicius Santos Lima },
publisher = {IEEE SigPort},
title = {Feature LMS Algorithms},
year = {2018} }
TY - EJOUR
T1 - Feature LMS Algorithms
AU - Paulo Sergio Ramirez Diniz; Hamed Yazdanpanah; Markus Vinicius Santos Lima
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2874
ER -
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, Markus Vinicius Santos Lima. (2018). Feature LMS Algorithms. IEEE SigPort. http://sigport.org/2874
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, Markus Vinicius Santos Lima, 2018. Feature LMS Algorithms. Available at: http://sigport.org/2874.
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, Markus Vinicius Santos Lima. (2018). "Feature LMS Algorithms." Web.
1. Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, Markus Vinicius Santos Lima. Feature LMS Algorithms [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2874

GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching

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Authors:
Zeyu Fu, Federico Angelini, Syed Mohsen Naqvi, Jonathon A. Chambers
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14 April 2018 - 4:15pm
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[1] Zeyu Fu, Federico Angelini, Syed Mohsen Naqvi, Jonathon A. Chambers, "GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2851. Accessed: May. 26, 2019.
@article{2851-18,
url = {http://sigport.org/2851},
author = {Zeyu Fu; Federico Angelini; Syed Mohsen Naqvi; Jonathon A. Chambers },
publisher = {IEEE SigPort},
title = {GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching},
year = {2018} }
TY - EJOUR
T1 - GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching
AU - Zeyu Fu; Federico Angelini; Syed Mohsen Naqvi; Jonathon A. Chambers
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2851
ER -
Zeyu Fu, Federico Angelini, Syed Mohsen Naqvi, Jonathon A. Chambers. (2018). GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching. IEEE SigPort. http://sigport.org/2851
Zeyu Fu, Federico Angelini, Syed Mohsen Naqvi, Jonathon A. Chambers, 2018. GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching. Available at: http://sigport.org/2851.
Zeyu Fu, Federico Angelini, Syed Mohsen Naqvi, Jonathon A. Chambers. (2018). "GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching." Web.
1. Zeyu Fu, Federico Angelini, Syed Mohsen Naqvi, Jonathon A. Chambers. GM-PHD Filter Based Online Multiple Human Tracking using Deep Discriminative Correlation Matching [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2851

Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster)


We propose a novel automatic shrinkage tuning technique for the adaptive proximal forward-backward splitting (APFBS) algorithm. The shrinkage tuning aims to choose an appropriate value of the shrinkage parameter and achieve minimal system mismatch as possible. The system mismatch is approximated based on time-averaged second-order statistics. Numerical examples show that the proposed method achieves performance fairly close to that with a manually chosen shrinkage parameter for colored input signals at some signal to noise ratio (SNR).

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Authors:
Kwangjin JEONG, Masahiro YUKAWA, Masao YAMAGISHI, Isao YAMADA
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14 April 2018 - 8:55am
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ICASSP2018Poster9_1p.pdf

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[1] Kwangjin JEONG, Masahiro YUKAWA, Masao YAMAGISHI, Isao YAMADA, "Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster)", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2830. Accessed: May. 26, 2019.
@article{2830-18,
url = {http://sigport.org/2830},
author = {Kwangjin JEONG; Masahiro YUKAWA; Masao YAMAGISHI; Isao YAMADA },
publisher = {IEEE SigPort},
title = {Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster)},
year = {2018} }
TY - EJOUR
T1 - Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster)
AU - Kwangjin JEONG; Masahiro YUKAWA; Masao YAMAGISHI; Isao YAMADA
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2830
ER -
Kwangjin JEONG, Masahiro YUKAWA, Masao YAMAGISHI, Isao YAMADA. (2018). Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster). IEEE SigPort. http://sigport.org/2830
Kwangjin JEONG, Masahiro YUKAWA, Masao YAMAGISHI, Isao YAMADA, 2018. Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster). Available at: http://sigport.org/2830.
Kwangjin JEONG, Masahiro YUKAWA, Masao YAMAGISHI, Isao YAMADA. (2018). "Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster)." Web.
1. Kwangjin JEONG, Masahiro YUKAWA, Masao YAMAGISHI, Isao YAMADA. Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering (Poster) [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2830

Feature LMS Algorithms


In recent years, there is a growing effort in the learning algorithms
area to propose new strategies to detect and exploit
sparsity in the model parameters. In many situations, the
sparsity is hidden in the relations among these coefficients
so that some suitable tools are required to reveal the potential
sparsity. This work proposes a set of LMS-type algorithms,
collectively called Feature LMS (F-LMS) algorithms, setting
forth a hidden feature of the unknown parameters, which ultimately
would improve convergence speed and steady-state

Paper Details

Authors:
Submitted On:
13 April 2018 - 11:20pm
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Type:
Event:
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ICASSP2018_Presentation

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[1] , "Feature LMS Algorithms", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2787. Accessed: May. 26, 2019.
@article{2787-18,
url = {http://sigport.org/2787},
author = { },
publisher = {IEEE SigPort},
title = {Feature LMS Algorithms},
year = {2018} }
TY - EJOUR
T1 - Feature LMS Algorithms
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2787
ER -
. (2018). Feature LMS Algorithms. IEEE SigPort. http://sigport.org/2787
, 2018. Feature LMS Algorithms. Available at: http://sigport.org/2787.
. (2018). "Feature LMS Algorithms." Web.
1. . Feature LMS Algorithms [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2787

CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA

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Authors:
Bruno Scalzo Dees, Yili Xia, Scott C. Douglas, Danilo P. Mandic
Submitted On:
13 April 2018 - 10:57am
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poster_MICCC.pdf

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[1] Bruno Scalzo Dees, Yili Xia, Scott C. Douglas, Danilo P. Mandic, "CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2715. Accessed: May. 26, 2019.
@article{2715-18,
url = {http://sigport.org/2715},
author = {Bruno Scalzo Dees; Yili Xia; Scott C. Douglas; Danilo P. Mandic },
publisher = {IEEE SigPort},
title = {CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA},
year = {2018} }
TY - EJOUR
T1 - CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA
AU - Bruno Scalzo Dees; Yili Xia; Scott C. Douglas; Danilo P. Mandic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2715
ER -
Bruno Scalzo Dees, Yili Xia, Scott C. Douglas, Danilo P. Mandic. (2018). CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA. IEEE SigPort. http://sigport.org/2715
Bruno Scalzo Dees, Yili Xia, Scott C. Douglas, Danilo P. Mandic, 2018. CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA. Available at: http://sigport.org/2715.
Bruno Scalzo Dees, Yili Xia, Scott C. Douglas, Danilo P. Mandic. (2018). "CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA." Web.
1. Bruno Scalzo Dees, Yili Xia, Scott C. Douglas, Danilo P. Mandic. CORRENTROPY-BASED ADAPTIVE FILTERING OF NONCIRCULAR COMPLEX DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2715

DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS


In this paper, we use the set-membership normalized least-mean-square (SM-NLMS) algorithm to censor the data set in big data applications. First, we use the distribution of the noise signal and the excess of the steady-state mean-square error (EMSE) to estimate the threshold for the desired update rate in the single threshold SM-NLMS (ST-SM-NLMS) algorithm. Then, we introduce the double threshold SM-NLMS (DT-SM-NLMS) algorithm which defines an acceptable
range of the error signal. This algorithm censors the data with very low and very high output estimation error.

Paper Details

Authors:
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah
Submitted On:
10 November 2017 - 12:09pm
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DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS

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[1] Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, "DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2295. Accessed: May. 26, 2019.
@article{2295-17,
url = {http://sigport.org/2295},
author = {Paulo Sergio Ramirez Diniz; Hamed Yazdanpanah },
publisher = {IEEE SigPort},
title = {DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS},
year = {2017} }
TY - EJOUR
T1 - DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS
AU - Paulo Sergio Ramirez Diniz; Hamed Yazdanpanah
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2295
ER -
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah. (2017). DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS. IEEE SigPort. http://sigport.org/2295
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah, 2017. DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS. Available at: http://sigport.org/2295.
Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah. (2017). "DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS." Web.
1. Paulo Sergio Ramirez Diniz, Hamed Yazdanpanah. DATA CENSORING WITH SET-MEMBERSHIP ALGORITHMS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2295

The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation

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Authors:
Hongbo Lin
Submitted On:
9 November 2017 - 9:17pm
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The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation

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[1] Hongbo Lin, "The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2277. Accessed: May. 26, 2019.
@article{2277-17,
url = {http://sigport.org/2277},
author = {Hongbo Lin },
publisher = {IEEE SigPort},
title = {The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation},
year = {2017} }
TY - EJOUR
T1 - The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation
AU - Hongbo Lin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2277
ER -
Hongbo Lin. (2017). The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation. IEEE SigPort. http://sigport.org/2277
Hongbo Lin, 2017. The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation. Available at: http://sigport.org/2277.
Hongbo Lin. (2017). "The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation." Web.
1. Hongbo Lin. The Adaptive Complex Shock Diffusion for Seismic Random Noise Attenuation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2277

D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS

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Authors:
Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei
Submitted On:
9 March 2017 - 1:10am
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[1] Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, "D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1716. Accessed: May. 26, 2019.
@article{1716-17,
url = {http://sigport.org/1716},
author = {Amir Daneshmand; Ying Sun; Gesualdo Scutari; Francisco Facchinei },
publisher = {IEEE SigPort},
title = {D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS},
year = {2017} }
TY - EJOUR
T1 - D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS
AU - Amir Daneshmand; Ying Sun; Gesualdo Scutari; Francisco Facchinei
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1716
ER -
Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei. (2017). D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS. IEEE SigPort. http://sigport.org/1716
Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei, 2017. D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS. Available at: http://sigport.org/1716.
Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei. (2017). "D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS." Web.
1. Amir Daneshmand, Ying Sun, Gesualdo Scutari, Francisco Facchinei. D2L: DECENTRALIZED DICTIONARY LEARNING OVER DYNAMIC NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1716

RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING


In this paper, we propose some sparsity aware algorithms, namely the Recursive least-Squares for sparse systems (S-RLS) and l0-norm Recursive least-Squares (l0-RLS), in order to exploit the sparsity of an unknown system. The first algorithm, applies a discard function on the weight vector to disregard the coefficients close to zero during the update process. The second algorithm, employs the sparsity-promoting scheme via some non-convex approximations to the l0-norm.

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Authors:
Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz
Submitted On:
3 March 2017 - 9:25pm
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RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING

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[1] Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz, "RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1621. Accessed: May. 26, 2019.
@article{1621-17,
url = {http://sigport.org/1621},
author = {Hamed Yazdanpanah; Paulo Sergio Ramirez Diniz },
publisher = {IEEE SigPort},
title = {RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING},
year = {2017} }
TY - EJOUR
T1 - RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING
AU - Hamed Yazdanpanah; Paulo Sergio Ramirez Diniz
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1621
ER -
Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz. (2017). RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING. IEEE SigPort. http://sigport.org/1621
Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz, 2017. RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING. Available at: http://sigport.org/1621.
Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz. (2017). "RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING." Web.
1. Hamed Yazdanpanah, Paulo Sergio Ramirez Diniz. RECURSIVE LEAST-SQUARES ALGORITHMS FOR SPARSE SYSTEM MODELING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1621

ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY


Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification. It is well-known that solving for the sparse coefficient vector to maximize these priors results in a hard non-convex and mixed integer programming problem. Most existing solutions to this optimization problem either involve simplifying assumptions/relaxations or are computationally expensive.

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Authors:
Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga
Submitted On:
27 February 2017 - 9:59pm
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Poster_ICASSP_2017_AMP.pdf

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[1] Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga, "ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1467. Accessed: May. 26, 2019.
@article{1467-17,
url = {http://sigport.org/1467},
author = {Tiep H. Vu; Hojjat S. Mousavi; Vishal Monga },
publisher = {IEEE SigPort},
title = {ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY},
year = {2017} }
TY - EJOUR
T1 - ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY
AU - Tiep H. Vu; Hojjat S. Mousavi; Vishal Monga
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1467
ER -
Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga. (2017). ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY. IEEE SigPort. http://sigport.org/1467
Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga, 2017. ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY. Available at: http://sigport.org/1467.
Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga. (2017). "ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY." Web.
1. Tiep H. Vu, Hojjat S. Mousavi, Vishal Monga. ADAPTIVE MATCHING PURSUIT FOR SPARSE SIGNAL RECOVERY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1467

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