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Emerging: Big Data

AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering


One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.

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Authors:
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero
Submitted On:
5 March 2017 - 11:06pm
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ICASSP_AMOS_2017.pdf

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[1] Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1643. Accessed: Aug. 18, 2017.
@article{1643-17,
url = {http://sigport.org/1643},
author = {Pin-Yu Chen; Thibaut Gensollen; Alfred Hero },
publisher = {IEEE SigPort},
title = {AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering},
year = {2017} }
TY - EJOUR
T1 - AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
AU - Pin-Yu Chen; Thibaut Gensollen; Alfred Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1643
ER -
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. IEEE SigPort. http://sigport.org/1643
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, 2017. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. Available at: http://sigport.org/1643.
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering." Web.
1. Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1643

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

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Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
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Summarization of Human Activity Videos Via Low-Rank Approximation

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1548. Accessed: Aug. 18, 2017.
@article{1548-17,
url = {http://sigport.org/1548},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1548
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1548
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1548.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1548

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
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Type:
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Summarization of Human Activity Videos Via Low-Rank Approximation

(196 downloads)

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1547. Accessed: Aug. 18, 2017.
@article{1547-17,
url = {http://sigport.org/1547},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1547
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1547
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1547.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1547

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(196 downloads)

Keywords

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1546. Accessed: Aug. 18, 2017.
@article{1546-17,
url = {http://sigport.org/1546},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1546
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1546
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1546.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1546

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(196 downloads)

Keywords

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[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1545. Accessed: Aug. 18, 2017.
@article{1545-17,
url = {http://sigport.org/1545},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1545
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1545
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1545.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1545

Summarization of Human Activity Videos Via Low-Rank Approximation


Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic

Paper Details

Authors:
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas
Submitted On:
1 March 2017 - 6:25am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Summarization of Human Activity Videos Via Low-Rank Approximation

(196 downloads)

Keywords

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Subscribe

[1] Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, "Summarization of Human Activity Videos Via Low-Rank Approximation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1544. Accessed: Aug. 18, 2017.
@article{1544-17,
url = {http://sigport.org/1544},
author = {Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas },
publisher = {IEEE SigPort},
title = {Summarization of Human Activity Videos Via Low-Rank Approximation},
year = {2017} }
TY - EJOUR
T1 - Summarization of Human Activity Videos Via Low-Rank Approximation
AU - Anastasios Tefas; Nikos Nikolaidis; Ioannis Pitas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1544
ER -
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). Summarization of Human Activity Videos Via Low-Rank Approximation. IEEE SigPort. http://sigport.org/1544
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas, 2017. Summarization of Human Activity Videos Via Low-Rank Approximation. Available at: http://sigport.org/1544.
Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. (2017). "Summarization of Human Activity Videos Via Low-Rank Approximation." Web.
1. Anastasios Tefas, Nikos Nikolaidis, Ioannis Pitas. Summarization of Human Activity Videos Via Low-Rank Approximation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1544

Fast and Stable Signal Deconvolution via Compressible State-Space Models


Objective: Common biological measurements are in
the form of noisy convolutions of signals of interest with possibly
unknown and transient blurring kernels. Examples include EEG
and calcium imaging data. Thus, signal deconvolution of these
measurements is crucial in understanding the underlying biological
processes. The objective of this paper is to develop fast and
stable solutions for signal deconvolution from noisy, blurred and
undersampled data, where the signals are in the form of discrete

Paper Details

Authors:
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
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FCSS_slides.pdf

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[1] Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, "Fast and Stable Signal Deconvolution via Compressible State-Space Models", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1438. Accessed: Aug. 18, 2017.
@article{1438-16,
url = {http://sigport.org/1438},
author = {Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Fast and Stable Signal Deconvolution via Compressible State-Space Models},
year = {2016} }
TY - EJOUR
T1 - Fast and Stable Signal Deconvolution via Compressible State-Space Models
AU - Abbas Kazemipour; Ji Liu; Min Wu ; Patrick Kanold and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1438
ER -
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). Fast and Stable Signal Deconvolution via Compressible State-Space Models. IEEE SigPort. http://sigport.org/1438
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi, 2016. Fast and Stable Signal Deconvolution via Compressible State-Space Models. Available at: http://sigport.org/1438.
Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. (2016). "Fast and Stable Signal Deconvolution via Compressible State-Space Models." Web.
1. Abbas Kazemipour, Ji Liu, Min Wu , Patrick Kanold and Behtash Babadi. Fast and Stable Signal Deconvolution via Compressible State-Space Models [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1438

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

Paper Details

Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
Submitted On:
8 December 2016 - 12:32am
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sketching-globalsip16-presentation.pdf

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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1420. Accessed: Aug. 18, 2017.
@article{1420-16,
url = {http://sigport.org/1420},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1420
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1420
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1420.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1420

Multilayer Spectral Graph Clustering via Convex Layer Aggregation


Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation.

Paper Details

Authors:
Pin-Yu Chen, Alfred Hero
Submitted On:
7 December 2016 - 10:03pm
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GlobalSIP_slides

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[1] Pin-Yu Chen, Alfred Hero, "Multilayer Spectral Graph Clustering via Convex Layer Aggregation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1418. Accessed: Aug. 18, 2017.
@article{1418-16,
url = {http://sigport.org/1418},
author = {Pin-Yu Chen; Alfred Hero },
publisher = {IEEE SigPort},
title = {Multilayer Spectral Graph Clustering via Convex Layer Aggregation},
year = {2016} }
TY - EJOUR
T1 - Multilayer Spectral Graph Clustering via Convex Layer Aggregation
AU - Pin-Yu Chen; Alfred Hero
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1418
ER -
Pin-Yu Chen, Alfred Hero. (2016). Multilayer Spectral Graph Clustering via Convex Layer Aggregation. IEEE SigPort. http://sigport.org/1418
Pin-Yu Chen, Alfred Hero, 2016. Multilayer Spectral Graph Clustering via Convex Layer Aggregation. Available at: http://sigport.org/1418.
Pin-Yu Chen, Alfred Hero. (2016). "Multilayer Spectral Graph Clustering via Convex Layer Aggregation." Web.
1. Pin-Yu Chen, Alfred Hero. Multilayer Spectral Graph Clustering via Convex Layer Aggregation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1418

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

Paper Details

Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
Submitted On:
8 December 2016 - 3:51pm
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Event:
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Document Files

sketching-globalsip16-presentation.pdf

(112 downloads)

Keywords

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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1415. Accessed: Aug. 18, 2017.
@article{1415-16,
url = {http://sigport.org/1415},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1415
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1415
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1415.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1415

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