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Emerging: Big Data

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

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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: May. 26, 2018.
@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,

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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: May. 26, 2018.
@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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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: May. 26, 2018.
@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.

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Authors:
Natanael Taufik, Alfan F. Wicaksono, Mirna Adriani
Submitted On:
22 November 2016 - 7:42am
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IALP2016 - Named Entity Recognition on Indonesian Microblog Messages.pdf

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[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. 26, 2018.
@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

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Authors:
Jing Xu, Xu Yang, Bin Xu
Submitted On:
18 November 2016 - 1:55am
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Valence-Arousal Ratings Prediction with Co-occurrence Word-embedding.pdf

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[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. 26, 2018.
@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.

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Authors:
Abbas Kazemipour, Min Wu and Behtash Babadi
Submitted On:
12 December 2016 - 9:35am
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Robust_SEPP_TSP.pdf

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[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. 26, 2018.
@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.

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Authors:
Fernando Gama, Santiago Segarra, Alejandro Ribeiro
Submitted On:
24 March 2016 - 4:45am
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cut-metrics-icassp16-presentation.pdf

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[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. 26, 2018.
@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

ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS

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23 March 2016 - 9:10pm
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icassp16_poster_ming_hou.pdf

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[1] , "ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1015. Accessed: May. 26, 2018.
@article{1015-16,
url = {http://sigport.org/1015},
author = { },
publisher = {IEEE SigPort},
title = {ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS},
year = {2016} }
TY - EJOUR
T1 - ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1015
ER -
. (2016). ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS. IEEE SigPort. http://sigport.org/1015
, 2016. ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS. Available at: http://sigport.org/1015.
. (2016). "ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS." Web.
1. . ONLINE INCREMENTAL HIGHER-ORDER PARTIAL LEAST SQUARES REGRESSION FOR FAST RECONSTRUCTION OF MOTION TRAJECTORIES FROM TENSOR STREAMS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1015

Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches

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Authors:
Eyal En Gad, Akshay Gadde, Salman Avestimehr, Antonio Ortega
Submitted On:
21 March 2016 - 7:53pm
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ppt_v2.pdf

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[1] Eyal En Gad, Akshay Gadde, Salman Avestimehr, Antonio Ortega, "Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/941. Accessed: May. 26, 2018.
@article{941-16,
url = {http://sigport.org/941},
author = {Eyal En Gad; Akshay Gadde; Salman Avestimehr; Antonio Ortega },
publisher = {IEEE SigPort},
title = {Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches},
year = {2016} }
TY - EJOUR
T1 - Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches
AU - Eyal En Gad; Akshay Gadde; Salman Avestimehr; Antonio Ortega
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/941
ER -
Eyal En Gad, Akshay Gadde, Salman Avestimehr, Antonio Ortega. (2016). Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches. IEEE SigPort. http://sigport.org/941
Eyal En Gad, Akshay Gadde, Salman Avestimehr, Antonio Ortega, 2016. Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches. Available at: http://sigport.org/941.
Eyal En Gad, Akshay Gadde, Salman Avestimehr, Antonio Ortega. (2016). "Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches." Web.
1. Eyal En Gad, Akshay Gadde, Salman Avestimehr, Antonio Ortega. Active Learning on Weighted Graphs Using Adaptive and Non-adaptive Approaches [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/941

BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS


china temperature graph

The observation of frequency folding in graph spectrum during down-sampling for signals on bipartite graphs—analogous to the same phenomenon in Fourier domain for regularly sampled signals—has led to the development of critically sampled wavelet filterbanks such as GraphBior. However, typical graph-signals live on general graphs that are not necessarily bipartite.

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Authors:
Jin Zeng, Gene Cheung, Antonio Ortega
Submitted On:
20 March 2016 - 10:58am
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ICASSP16_Jin.pdf

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[1] Jin Zeng, Gene Cheung, Antonio Ortega, "BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/878. Accessed: May. 26, 2018.
@article{878-16,
url = {http://sigport.org/878},
author = {Jin Zeng; Gene Cheung; Antonio Ortega },
publisher = {IEEE SigPort},
title = {BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS},
year = {2016} }
TY - EJOUR
T1 - BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS
AU - Jin Zeng; Gene Cheung; Antonio Ortega
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/878
ER -
Jin Zeng, Gene Cheung, Antonio Ortega. (2016). BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS. IEEE SigPort. http://sigport.org/878
Jin Zeng, Gene Cheung, Antonio Ortega, 2016. BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS. Available at: http://sigport.org/878.
Jin Zeng, Gene Cheung, Antonio Ortega. (2016). "BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS." Web.
1. Jin Zeng, Gene Cheung, Antonio Ortega. BIPARTITE SUBGRAPH DECOMPOSITION FOR CRITICALLY SAMPLED WAVELET FILTERBANKS ON ARBITARY GRAPHS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/878

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