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Emerging: Big Data

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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[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: Feb. 25, 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: Feb. 25, 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.

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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: Feb. 25, 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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Document Files

sketching-globalsip16-presentation.pdf

(23 downloads)

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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: Feb. 25, 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

Scalable and Robust PCA Approach with Random Column/Row Sampling

Paper Details

Authors:
Mostafa Rahmani, George Atia
Submitted On:
7 December 2016 - 4:30pm
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Globalsip_RRD.pdf

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[1] Mostafa Rahmani, George Atia, "Scalable and Robust PCA Approach with Random Column/Row Sampling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1414. Accessed: Feb. 25, 2017.
@article{1414-16,
url = {http://sigport.org/1414},
author = {Mostafa Rahmani; George Atia },
publisher = {IEEE SigPort},
title = {Scalable and Robust PCA Approach with Random Column/Row Sampling},
year = {2016} }
TY - EJOUR
T1 - Scalable and Robust PCA Approach with Random Column/Row Sampling
AU - Mostafa Rahmani; George Atia
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1414
ER -
Mostafa Rahmani, George Atia. (2016). Scalable and Robust PCA Approach with Random Column/Row Sampling. IEEE SigPort. http://sigport.org/1414
Mostafa Rahmani, George Atia, 2016. Scalable and Robust PCA Approach with Random Column/Row Sampling. Available at: http://sigport.org/1414.
Mostafa Rahmani, George Atia. (2016). "Scalable and Robust PCA Approach with Random Column/Row Sampling." Web.
1. Mostafa Rahmani, George Atia. Scalable and Robust PCA Approach with Random Column/Row Sampling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1414

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

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Submitted On:
10 December 2016 - 3:39pm
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Slides_GlobalSIP.pdf

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1383. Accessed: Feb. 25, 2017.
@article{1383-16,
url = {http://sigport.org/1383},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1383
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1383
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1383.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1383

Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision


This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by $O(\frac{1}{n})$, the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size $O(\sqrt{\frac{\log n}{n^3}} \sigma)$ of one of the true frequencies.

Paper Details

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Submitted On:
10 December 2016 - 3:38pm
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Slides_GlobalSIP.pdf

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[1] , "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1382. Accessed: Feb. 25, 2017.
@article{1382-16,
url = {http://sigport.org/1382},
author = { },
publisher = {IEEE SigPort},
title = {Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision},
year = {2016} }
TY - EJOUR
T1 - Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1382
ER -
. (2016). Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. IEEE SigPort. http://sigport.org/1382
, 2016. Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision. Available at: http://sigport.org/1382.
. (2016). "Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision." Web.
1. . Approximate Support Recovery of Atomic Line Spectral Estimation: A Tale of Resolution and Precision [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1382

A Projection-free Decentralized Algorithm for Non-convex Optimization


This paper considers a decentralized projection free algorithm for non-convex optimization in high dimension. More specifically, we propose a Decentralized Frank-Wolfe (DeFW)
algorithm which is suitable when high dimensional optimization constraints are difficult to handle by conventional projection/proximal-based gradient descent methods. We present conditions under which the DeFW algorithm converges to a stationary point and prove that the rate of convergence is as fast as ${\cal O}( 1/\sqrt{T} )$, where

Paper Details

Authors:
Anna Scaglione, Jean Lafond, Eric Moulines
Submitted On:
7 December 2016 - 11:58pm
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ncvx_globalsip16.pdf

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[1] Anna Scaglione, Jean Lafond, Eric Moulines, "A Projection-free Decentralized Algorithm for Non-convex Optimization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1381. Accessed: Feb. 25, 2017.
@article{1381-16,
url = {http://sigport.org/1381},
author = {Anna Scaglione; Jean Lafond; Eric Moulines },
publisher = {IEEE SigPort},
title = {A Projection-free Decentralized Algorithm for Non-convex Optimization},
year = {2016} }
TY - EJOUR
T1 - A Projection-free Decentralized Algorithm for Non-convex Optimization
AU - Anna Scaglione; Jean Lafond; Eric Moulines
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1381
ER -
Anna Scaglione, Jean Lafond, Eric Moulines. (2016). A Projection-free Decentralized Algorithm for Non-convex Optimization. IEEE SigPort. http://sigport.org/1381
Anna Scaglione, Jean Lafond, Eric Moulines, 2016. A Projection-free Decentralized Algorithm for Non-convex Optimization. Available at: http://sigport.org/1381.
Anna Scaglione, Jean Lafond, Eric Moulines. (2016). "A Projection-free Decentralized Algorithm for Non-convex Optimization." Web.
1. Anna Scaglione, Jean Lafond, Eric Moulines. A Projection-free Decentralized Algorithm for Non-convex Optimization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1381

Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations


Submodular maximization problems belong to the family of combinatorial optimization problems and enjoy wide applications. In this paper, we focus on the problem of maximizing a monotone submodular function subject to a d-knapsack constraint, for which we propose a streaming algorithm that
achieves a (1/1+2d − ε) -approximation of the optimal value,

Paper Details

Authors:
Qilian Yu, Easton Li Xu, Shuguang Cui
Submitted On:
2 December 2016 - 10:41pm
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Lecture Slides

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[1] Qilian Yu, Easton Li Xu, Shuguang Cui, "Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1336. Accessed: Feb. 25, 2017.
@article{1336-16,
url = {http://sigport.org/1336},
author = {Qilian Yu; Easton Li Xu; Shuguang Cui },
publisher = {IEEE SigPort},
title = {Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations},
year = {2016} }
TY - EJOUR
T1 - Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations
AU - Qilian Yu; Easton Li Xu; Shuguang Cui
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1336
ER -
Qilian Yu, Easton Li Xu, Shuguang Cui. (2016). Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations. IEEE SigPort. http://sigport.org/1336
Qilian Yu, Easton Li Xu, Shuguang Cui, 2016. Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations. Available at: http://sigport.org/1336.
Qilian Yu, Easton Li Xu, Shuguang Cui. (2016). "Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations." Web.
1. Qilian Yu, Easton Li Xu, Shuguang Cui. Submodular Maximization with Multi-Knapsack Constraints and its Applications in Scientific Literature Recommendations [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1336

I-LoViT: Indoor Localization by Vibration Tracking


Signal processing techniques can create new applications for the data captured by existing sensor systems. Decades old sensor technology for monitoring the structural health of a building can serve a new role as a novel source of indoor localization data. Specifically, when a person's footstep-generated floor vibrations can be detected and located then it is possible to locate persons moving within a building. This emergent cyber-physical system holds the potential for an ambient localization service.

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Submitted On:
27 November 2016 - 11:17am
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I-LoViT briefing slides

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[1] , "I-LoViT: Indoor Localization by Vibration Tracking", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1313. Accessed: Feb. 25, 2017.
@article{1313-16,
url = {http://sigport.org/1313},
author = { },
publisher = {IEEE SigPort},
title = {I-LoViT: Indoor Localization by Vibration Tracking},
year = {2016} }
TY - EJOUR
T1 - I-LoViT: Indoor Localization by Vibration Tracking
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1313
ER -
. (2016). I-LoViT: Indoor Localization by Vibration Tracking. IEEE SigPort. http://sigport.org/1313
, 2016. I-LoViT: Indoor Localization by Vibration Tracking. Available at: http://sigport.org/1313.
. (2016). "I-LoViT: Indoor Localization by Vibration Tracking." Web.
1. . I-LoViT: Indoor Localization by Vibration Tracking [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1313

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