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Statistical Signal Processing

Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory


In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In
the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced
into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and,

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Authors:
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri
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2 March 2017 - 1:17pm
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[1] Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri, "Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1588. Accessed: Jul. 20, 2019.
@article{1588-17,
url = {http://sigport.org/1588},
author = {Mohamed Suliman; Tarig Ballal; Abla Kammoun; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory},
year = {2017} }
TY - EJOUR
T1 - Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory
AU - Mohamed Suliman; Tarig Ballal; Abla Kammoun; Tareq Y. Al-Naffouri
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1588
ER -
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri. (2017). Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory. IEEE SigPort. http://sigport.org/1588
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri, 2017. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory. Available at: http://sigport.org/1588.
Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri. (2017). "Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory." Web.
1. Mohamed Suliman, Tarig Ballal, Abla Kammoun, Tareq Y. Al-Naffouri. Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1588

Weak Law of Large Numbers for Stationary Graph Processes


The ability to obtain accurate estimators from a set of measurements is a key factor in science and engineering. Typically, there is an inherent assumption that the measurements were taken in a sequential order, be it in space or time. However, data is increasingly irregular so this assumption of sequentially obtained measurements no longer holds. By leveraging notions of graph signal processing to account for these irregular domains, we propose an unbiased estimator for the mean of a wide sense stationary graph process based on the diffusion of a single realization.

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Authors:
Fernando Gama, Alejandro Ribeiro
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2 March 2017 - 9:47am
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[1] Fernando Gama, Alejandro Ribeiro, "Weak Law of Large Numbers for Stationary Graph Processes", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1585. Accessed: Jul. 20, 2019.
@article{1585-17,
url = {http://sigport.org/1585},
author = {Fernando Gama; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Weak Law of Large Numbers for Stationary Graph Processes},
year = {2017} }
TY - EJOUR
T1 - Weak Law of Large Numbers for Stationary Graph Processes
AU - Fernando Gama; Alejandro Ribeiro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1585
ER -
Fernando Gama, Alejandro Ribeiro. (2017). Weak Law of Large Numbers for Stationary Graph Processes. IEEE SigPort. http://sigport.org/1585
Fernando Gama, Alejandro Ribeiro, 2017. Weak Law of Large Numbers for Stationary Graph Processes. Available at: http://sigport.org/1585.
Fernando Gama, Alejandro Ribeiro. (2017). "Weak Law of Large Numbers for Stationary Graph Processes." Web.
1. Fernando Gama, Alejandro Ribeiro. Weak Law of Large Numbers for Stationary Graph Processes [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1585

Estimation accuracy of non-standard maximum likelihood estimators

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Authors:
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal
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1 March 2017 - 5:08am
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[1] Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal, "Estimation accuracy of non-standard maximum likelihood estimators", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1542. Accessed: Jul. 20, 2019.
@article{1542-17,
url = {http://sigport.org/1542},
author = {Nabil Kbayer; Jerome Galy; Eric Chaumette; Francois Vincent; Alexandre Renaux; Pascal Larzabal },
publisher = {IEEE SigPort},
title = {Estimation accuracy of non-standard maximum likelihood estimators},
year = {2017} }
TY - EJOUR
T1 - Estimation accuracy of non-standard maximum likelihood estimators
AU - Nabil Kbayer; Jerome Galy; Eric Chaumette; Francois Vincent; Alexandre Renaux; Pascal Larzabal
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1542
ER -
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal. (2017). Estimation accuracy of non-standard maximum likelihood estimators. IEEE SigPort. http://sigport.org/1542
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal, 2017. Estimation accuracy of non-standard maximum likelihood estimators. Available at: http://sigport.org/1542.
Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal. (2017). "Estimation accuracy of non-standard maximum likelihood estimators." Web.
1. Nabil Kbayer, Jerome Galy, Eric Chaumette, Francois Vincent, Alexandre Renaux, Pascal Larzabal. Estimation accuracy of non-standard maximum likelihood estimators [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1542

Generalized Barankin-Type Lower Bounds for Misspecified Models

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Authors:
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent
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1 March 2017 - 5:03am
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[1] Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent, "Generalized Barankin-Type Lower Bounds for Misspecified Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1541. Accessed: Jul. 20, 2019.
@article{1541-17,
url = {http://sigport.org/1541},
author = {Mouhamadou Lamine Diong; Eric Chaumette; Francois Vincent },
publisher = {IEEE SigPort},
title = {Generalized Barankin-Type Lower Bounds for Misspecified Models},
year = {2017} }
TY - EJOUR
T1 - Generalized Barankin-Type Lower Bounds for Misspecified Models
AU - Mouhamadou Lamine Diong; Eric Chaumette; Francois Vincent
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1541
ER -
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent. (2017). Generalized Barankin-Type Lower Bounds for Misspecified Models. IEEE SigPort. http://sigport.org/1541
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent, 2017. Generalized Barankin-Type Lower Bounds for Misspecified Models. Available at: http://sigport.org/1541.
Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent. (2017). "Generalized Barankin-Type Lower Bounds for Misspecified Models." Web.
1. Mouhamadou Lamine Diong, Eric Chaumette, Francois Vincent. Generalized Barankin-Type Lower Bounds for Misspecified Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1541

Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference

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Authors:
Eric Chaumette, Francois Vincent
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1 March 2017 - 4:59am
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[1] Eric Chaumette, Francois Vincent, "Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1540. Accessed: Jul. 20, 2019.
@article{1540-17,
url = {http://sigport.org/1540},
author = {Eric Chaumette; Francois Vincent },
publisher = {IEEE SigPort},
title = {Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference},
year = {2017} }
TY - EJOUR
T1 - Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference
AU - Eric Chaumette; Francois Vincent
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1540
ER -
Eric Chaumette, Francois Vincent. (2017). Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference. IEEE SigPort. http://sigport.org/1540
Eric Chaumette, Francois Vincent, 2017. Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference. Available at: http://sigport.org/1540.
Eric Chaumette, Francois Vincent. (2017). "Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference." Web.
1. Eric Chaumette, Francois Vincent. Concomitant of Ordered Multivariate Normal Distribution with Application to Parametric Inference [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1540

Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval


The recently reported Wirtinger flow (WF) algorithm has been demonstrated as a promising method for solving the problem of phase retrieval by applying a gradient descent scheme. An empirical choice of stepsize is suggested in practice. However, this heuristic stepsize selection rule is not optimal. In order to accelerate the convergence rate, we propose an improved WF with optimal stepsize. It is revealed that this optimal stepsize is the solution of a univariate cubic equation with real-valued coefficients.

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Authors:
Sreeraman Rajan, Xingzhao Liu
Submitted On:
28 February 2017 - 3:58am
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[1] Sreeraman Rajan, Xingzhao Liu, "Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1488. Accessed: Jul. 20, 2019.
@article{1488-17,
url = {http://sigport.org/1488},
author = {Sreeraman Rajan; Xingzhao Liu },
publisher = {IEEE SigPort},
title = {Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval},
year = {2017} }
TY - EJOUR
T1 - Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval
AU - Sreeraman Rajan; Xingzhao Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1488
ER -
Sreeraman Rajan, Xingzhao Liu. (2017). Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval. IEEE SigPort. http://sigport.org/1488
Sreeraman Rajan, Xingzhao Liu, 2017. Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval. Available at: http://sigport.org/1488.
Sreeraman Rajan, Xingzhao Liu. (2017). "Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval." Web.
1. Sreeraman Rajan, Xingzhao Liu. Wirtinger Flow Method with Optimal Stepsize for Phase Retrieval [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1488

RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY

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27 February 2017 - 2:49pm
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[1] , "RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1444. Accessed: Jul. 20, 2019.
@article{1444-17,
url = {http://sigport.org/1444},
author = { },
publisher = {IEEE SigPort},
title = {RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY},
year = {2017} }
TY - EJOUR
T1 - RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1444
ER -
. (2017). RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY. IEEE SigPort. http://sigport.org/1444
, 2017. RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY. Available at: http://sigport.org/1444.
. (2017). "RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY." Web.
1. . RECURRENT LATENT VARIABLE CONDITIONAL HETEROSCEDASTICITY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1444

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
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7 December 2016 - 10:03pm
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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: Jul. 20, 2019.
@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

Robust Regularized Least-Squares Beamforming Approach to Signal Estimation


In this paper, we address the problem of robust adaptive beamforming of signals received by a linear array. The challenge associated with the beamforming problem is twofold. Firstly, the process requires the inversion of the usually ill-conditioned covariance matrix of the received signals. Secondly, the steering vector pertaining to the direction of arrival of the signal of interest is not known precisely. To tackle these two challenges, the standard capon beamformer is manipulated to a form where the beamformer output is obtained as a scaled version of the inner product of two vectors.

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Authors:
Tarig Ballal, Tareq Y. Al-Naffouri
Submitted On:
7 December 2016 - 1:12pm
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[1] Tarig Ballal, Tareq Y. Al-Naffouri, "Robust Regularized Least-Squares Beamforming Approach to Signal Estimation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1410. Accessed: Jul. 20, 2019.
@article{1410-16,
url = {http://sigport.org/1410},
author = {Tarig Ballal; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {Robust Regularized Least-Squares Beamforming Approach to Signal Estimation},
year = {2016} }
TY - EJOUR
T1 - Robust Regularized Least-Squares Beamforming Approach to Signal Estimation
AU - Tarig Ballal; Tareq Y. Al-Naffouri
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1410
ER -
Tarig Ballal, Tareq Y. Al-Naffouri. (2016). Robust Regularized Least-Squares Beamforming Approach to Signal Estimation. IEEE SigPort. http://sigport.org/1410
Tarig Ballal, Tareq Y. Al-Naffouri, 2016. Robust Regularized Least-Squares Beamforming Approach to Signal Estimation. Available at: http://sigport.org/1410.
Tarig Ballal, Tareq Y. Al-Naffouri. (2016). "Robust Regularized Least-Squares Beamforming Approach to Signal Estimation." Web.
1. Tarig Ballal, Tareq Y. Al-Naffouri. Robust Regularized Least-Squares Beamforming Approach to Signal Estimation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1410

FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS

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Authors:
ashkan esmaeili (1712), Arash Amini, Farokh Marvasti
Submitted On:
8 December 2016 - 3:51pm
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[1] ashkan esmaeili (1712), Arash Amini, Farokh Marvasti, "FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1405. Accessed: Jul. 20, 2019.
@article{1405-16,
url = {http://sigport.org/1405},
author = {ashkan esmaeili (1712); Arash Amini; Farokh Marvasti },
publisher = {IEEE SigPort},
title = {FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS},
year = {2016} }
TY - EJOUR
T1 - FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS
AU - ashkan esmaeili (1712); Arash Amini; Farokh Marvasti
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1405
ER -
ashkan esmaeili (1712), Arash Amini, Farokh Marvasti. (2016). FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS. IEEE SigPort. http://sigport.org/1405
ashkan esmaeili (1712), Arash Amini, Farokh Marvasti, 2016. FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS. Available at: http://sigport.org/1405.
ashkan esmaeili (1712), Arash Amini, Farokh Marvasti. (2016). "FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS." Web.
1. ashkan esmaeili (1712), Arash Amini, Farokh Marvasti. FAST METHODS FOR RECOVERING SPARSE PARAMETERS IN LINEAR LOW RANK MODELS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1405

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