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Emerging: Big Data

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
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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: Oct. 19, 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.

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Authors:
Abbas Kazemipour, Min Wu and Behtash Babadi
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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: Oct. 19, 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.

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Authors:
Fernando Gama, Santiago Segarra, Alejandro Ribeiro
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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: Oct. 19, 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

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: Oct. 19, 2017.
@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
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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: Oct. 19, 2017.
@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: Oct. 19, 2017.
@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

Distributed Estimation via Paid Crowd Work


Consider a distributed estimation problem to be carried out by paid crowdworkers, where results are to be returned quickly and accurately. Estimation accuracy is a function of the number of workers completing the job and of the quality of the workers, both of which may be influenced by the payment offered. With limited budget, payment allocation should consider both effects to obtain best results. Since people are not deterministic, payment offers will lead to a random number of variable-quality workers, as governed by choice models.

poster.pdf

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Authors:
Song Jianhan, Vei Wang Isaac Phua, and Lav R. Varshney
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19 March 2016 - 8:40pm
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poster.pdf

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[1] Song Jianhan, Vei Wang Isaac Phua, and Lav R. Varshney, "Distributed Estimation via Paid Crowd Work", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/840. Accessed: Oct. 19, 2017.
@article{840-16,
url = {http://sigport.org/840},
author = {Song Jianhan; Vei Wang Isaac Phua; and Lav R. Varshney },
publisher = {IEEE SigPort},
title = {Distributed Estimation via Paid Crowd Work},
year = {2016} }
TY - EJOUR
T1 - Distributed Estimation via Paid Crowd Work
AU - Song Jianhan; Vei Wang Isaac Phua; and Lav R. Varshney
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/840
ER -
Song Jianhan, Vei Wang Isaac Phua, and Lav R. Varshney. (2016). Distributed Estimation via Paid Crowd Work. IEEE SigPort. http://sigport.org/840
Song Jianhan, Vei Wang Isaac Phua, and Lav R. Varshney, 2016. Distributed Estimation via Paid Crowd Work. Available at: http://sigport.org/840.
Song Jianhan, Vei Wang Isaac Phua, and Lav R. Varshney. (2016). "Distributed Estimation via Paid Crowd Work." Web.
1. Song Jianhan, Vei Wang Isaac Phua, and Lav R. Varshney. Distributed Estimation via Paid Crowd Work [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/840

Introduction to the Special Session on Topological Data Analysis


Topological Data Analysis (TDA) is a topic which has recently seen many applications. The goal of this special session is to highlight the bridge between signal processing, machine learning and techniques in topological data analysis. In this way, we hope to encourage more engineers to start exploring TDA and its applications. This paper briefly introduces the standard techniques used in this area, delineates the common theme connecting the works presented in this session, and concludes with a brief summary of each of the papers presented.

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Authors:
Harish Chintakunta, Michael Robinson, Hamid Krim
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19 March 2016 - 8:29pm
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20160324_ICASSP.pdf

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[1] Harish Chintakunta, Michael Robinson, Hamid Krim, "Introduction to the Special Session on Topological Data Analysis", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/839. Accessed: Oct. 19, 2017.
@article{839-16,
url = {http://sigport.org/839},
author = {Harish Chintakunta; Michael Robinson; Hamid Krim },
publisher = {IEEE SigPort},
title = {Introduction to the Special Session on Topological Data Analysis},
year = {2016} }
TY - EJOUR
T1 - Introduction to the Special Session on Topological Data Analysis
AU - Harish Chintakunta; Michael Robinson; Hamid Krim
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/839
ER -
Harish Chintakunta, Michael Robinson, Hamid Krim. (2016). Introduction to the Special Session on Topological Data Analysis. IEEE SigPort. http://sigport.org/839
Harish Chintakunta, Michael Robinson, Hamid Krim, 2016. Introduction to the Special Session on Topological Data Analysis. Available at: http://sigport.org/839.
Harish Chintakunta, Michael Robinson, Hamid Krim. (2016). "Introduction to the Special Session on Topological Data Analysis." Web.
1. Harish Chintakunta, Michael Robinson, Hamid Krim. Introduction to the Special Session on Topological Data Analysis [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/839

D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization

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Authors:
Jean Lafond, Hoi-To Wai, Eric Moulines
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18 March 2016 - 2:56pm
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[1] Jean Lafond, Hoi-To Wai, Eric Moulines, "D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/775. Accessed: Oct. 19, 2017.
@article{775-16,
url = {http://sigport.org/775},
author = {Jean Lafond; Hoi-To Wai; Eric Moulines },
publisher = {IEEE SigPort},
title = {D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization},
year = {2016} }
TY - EJOUR
T1 - D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization
AU - Jean Lafond; Hoi-To Wai; Eric Moulines
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/775
ER -
Jean Lafond, Hoi-To Wai, Eric Moulines. (2016). D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization. IEEE SigPort. http://sigport.org/775
Jean Lafond, Hoi-To Wai, Eric Moulines, 2016. D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization. Available at: http://sigport.org/775.
Jean Lafond, Hoi-To Wai, Eric Moulines. (2016). "D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization." Web.
1. Jean Lafond, Hoi-To Wai, Eric Moulines. D-FW: Communication Efficient Distributed Algorithms for High-dimensional Sparse Optimization [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/775

Effecient Sensor Position Selection Using Graph Signal Sampling Theory


We consider the problem of selecting optimal sensor placements. The proposed approach is based on the sampling theorem of graph signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the existing methods in the context of graph signal processing and clarify the relationship between these methods and the proposed approach. The effectiveness of our approach is verified through numerical experiments, showing advantages in prediction error and execution time.

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Authors:
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega
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18 March 2016 - 2:54am
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Sakiyama_etal_ICASSP2016_3.pdf

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[1] Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega, "Effecient Sensor Position Selection Using Graph Signal Sampling Theory", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/761. Accessed: Oct. 19, 2017.
@article{761-16,
url = {http://sigport.org/761},
author = {Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega },
publisher = {IEEE SigPort},
title = {Effecient Sensor Position Selection Using Graph Signal Sampling Theory},
year = {2016} }
TY - EJOUR
T1 - Effecient Sensor Position Selection Using Graph Signal Sampling Theory
AU - Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/761
ER -
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega. (2016). Effecient Sensor Position Selection Using Graph Signal Sampling Theory. IEEE SigPort. http://sigport.org/761
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega, 2016. Effecient Sensor Position Selection Using Graph Signal Sampling Theory. Available at: http://sigport.org/761.
Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega. (2016). "Effecient Sensor Position Selection Using Graph Signal Sampling Theory." Web.
1. Yuichi Tanaka; Toshihisa Tanaka; Antonio Ortega. Effecient Sensor Position Selection Using Graph Signal Sampling Theory [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/761

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