Sorry, you need to enable JavaScript to visit this website.

Emerging: Big Data

Scalable and Robust PCA Approach with Random Column/Row Sampling

Paper Details

Authors:
Mostafa Rahmani, George Atia
Submitted On:
7 December 2016 - 4:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Globalsip_RRD.pdf

(55 downloads)

Keywords

Subscribe

[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: May. 24, 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.

Paper Details

Authors:
Submitted On:
10 December 2016 - 3:39pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Slides_GlobalSIP.pdf

(66 downloads)

Keywords

Subscribe

[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: May. 24, 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

Authors:
Submitted On:
10 December 2016 - 3:38pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Slides_GlobalSIP.pdf

(58 downloads)

Keywords

Subscribe

[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: May. 24, 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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ncvx_globalsip16.pdf

(74 downloads)

Keywords

Subscribe

[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: May. 24, 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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Lecture Slides

(63 downloads)

Keywords

Subscribe

[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: May. 24, 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.

Paper Details

Authors:
Submitted On:
27 November 2016 - 11:17am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

I-LoViT briefing slides

(60 downloads)

Keywords

Additional Categories

Subscribe

[1] , "I-LoViT: Indoor Localization by Vibration Tracking", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1313. Accessed: May. 24, 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

Named Entity Recognition on Indonesian Microblog Messages


This paper describes a model to address the task of named-entity recognition on Indonesian microblog messages due to its usefulness for higher-level tasks or text mining applications on Indonesian microblogs. We view our task as a sequence labeling problem using machine learning approach. We also propose various word-level and orthographic features, including the ones that are specific to the Indonesian language. Finally, in our experiment, we compared our model with a baseline model previously proposed for Indonesian formal documents, instead of microblog messages.

Paper Details

Authors:
Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani
Submitted On:
22 November 2016 - 7:42am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

IALP2016 - Named Entity Recognition on Indonesian Microblog Messages.pdf

(74 downloads)

Keywords

Additional Categories

Subscribe

[1] Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani, "Named Entity Recognition on Indonesian Microblog Messages", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1293. Accessed: May. 24, 2017.
@article{1293-16,
url = {http://sigport.org/1293},
author = {Natanael Taufik; Alfan F. Wicaksono; Mirna Adriani },
publisher = {IEEE SigPort},
title = {Named Entity Recognition on Indonesian Microblog Messages},
year = {2016} }
TY - EJOUR
T1 - Named Entity Recognition on Indonesian Microblog Messages
AU - Natanael Taufik; Alfan F. Wicaksono; Mirna Adriani
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1293
ER -
Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani. (2016). Named Entity Recognition on Indonesian Microblog Messages. IEEE SigPort. http://sigport.org/1293
Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani, 2016. Named Entity Recognition on Indonesian Microblog Messages. Available at: http://sigport.org/1293.
Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani. (2016). "Named Entity Recognition on Indonesian Microblog Messages." Web.
1. Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani. Named Entity Recognition on Indonesian Microblog Messages [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1293

Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding


Sentiment analysis draws increasing attention of researchers in wide-ranging fields. Compared with the commonly-used categorical

Paper Details

Authors:
Jing Xu, Xu Yang, Bin Xu
Submitted On:
18 November 2016 - 1:55am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding.pdf

(51 downloads)

Keywords

Subscribe

[1] Jing Xu, Xu Yang, Bin Xu, "Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1270. Accessed: May. 24, 2017.
@article{1270-16,
url = {http://sigport.org/1270},
author = {Jing Xu; Xu Yang; Bin Xu },
publisher = {IEEE SigPort},
title = {Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding},
year = {2016} }
TY - EJOUR
T1 - Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding
AU - Jing Xu; Xu Yang; Bin Xu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1270
ER -
Jing Xu, Xu Yang, Bin Xu. (2016). Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding. IEEE SigPort. http://sigport.org/1270
Jing Xu, Xu Yang, Bin Xu, 2016. Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding. Available at: http://sigport.org/1270.
Jing Xu, Xu Yang, Bin Xu. (2016). "Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding." Web.
1. Jing Xu, Xu Yang, Bin Xu. Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1270

Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling


We consider the problem of estimating discrete self- exciting point process models from limited binary observations, where the history of the process serves as the covariate. We analyze the performance of two classes of estimators: l1-regularized maximum likelihood and greedy estimation for a discrete version of the Hawkes process and characterize the sampling tradeoffs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear and generalized linear models with i.i.d.

Paper Details

Authors:
Abbas Kazemipour, Min Wu and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
Short Link:
Type:
Document Year:
Cite

Document Files

Robust_SEPP_TSP.pdf

(52 downloads)

Keywords

Subscribe

[1] Abbas Kazemipour, Min Wu and Behtash Babadi, "Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1261. Accessed: May. 24, 2017.
@article{1261-16,
url = {http://sigport.org/1261},
author = {Abbas Kazemipour; Min Wu and Behtash Babadi },
publisher = {IEEE SigPort},
title = {Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling},
year = {2016} }
TY - EJOUR
T1 - Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling
AU - Abbas Kazemipour; Min Wu and Behtash Babadi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1261
ER -
Abbas Kazemipour, Min Wu and Behtash Babadi. (2016). Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling. IEEE SigPort. http://sigport.org/1261
Abbas Kazemipour, Min Wu and Behtash Babadi, 2016. Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling. Available at: http://sigport.org/1261.
Abbas Kazemipour, Min Wu and Behtash Babadi. (2016). "Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling." Web.
1. Abbas Kazemipour, Min Wu and Behtash Babadi. Robust Estimation of Self-Exciting Point Process Models with Application to Neuronal Modeling [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1261

Overlapping Clustering of Network Data Using Cut Metrics


We present a novel method to hierarchically cluster networked data allowing nodes to simultaneously belong to multiple clusters. Given a network, our method outputs a cut metric on the underlying node set, which can be related to data coverings at different resolutions. The cut metric is obtained by averaging a set of ultrametrics, which are themselves the output of (non-overlapping) hierarchically clustering noisy versions of the original network of interest. The resulting algorithm is illustrated in synthetic networks and is used to classify handwritten digits from the MNIST database.

Paper Details

Authors:
Fernando Gama, Santiago Segarra, Alejandro Ribeiro
Submitted On:
24 March 2016 - 4:45am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

cut-metrics-icassp16-presentation.pdf

(153 downloads)

Keywords

Additional Categories

Subscribe

[1] Fernando Gama, Santiago Segarra, Alejandro Ribeiro, "Overlapping Clustering of Network Data Using Cut Metrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1020. Accessed: May. 24, 2017.
@article{1020-16,
url = {http://sigport.org/1020},
author = {Fernando Gama; Santiago Segarra; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Overlapping Clustering of Network Data Using Cut Metrics},
year = {2016} }
TY - EJOUR
T1 - Overlapping Clustering of Network Data Using Cut Metrics
AU - Fernando Gama; Santiago Segarra; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1020
ER -
Fernando Gama, Santiago Segarra, Alejandro Ribeiro. (2016). Overlapping Clustering of Network Data Using Cut Metrics. IEEE SigPort. http://sigport.org/1020
Fernando Gama, Santiago Segarra, Alejandro Ribeiro, 2016. Overlapping Clustering of Network Data Using Cut Metrics. Available at: http://sigport.org/1020.
Fernando Gama, Santiago Segarra, Alejandro Ribeiro. (2016). "Overlapping Clustering of Network Data Using Cut Metrics." Web.
1. Fernando Gama, Santiago Segarra, Alejandro Ribeiro. Overlapping Clustering of Network Data Using Cut Metrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1020

Pages