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Statistical Signal Processing

Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing

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Authors:
Ali Bereyhi, Ralf R. Müller
Submitted On:
12 April 2018 - 4:59pm
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ICASSP_Poster.pdf

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[1] Ali Bereyhi, Ralf R. Müller, "Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2502. Accessed: Sep. 20, 2018.
@article{2502-18,
url = {http://sigport.org/2502},
author = {Ali Bereyhi; Ralf R. Müller },
publisher = {IEEE SigPort},
title = {Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing},
year = {2018} }
TY - EJOUR
T1 - Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing
AU - Ali Bereyhi; Ralf R. Müller
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2502
ER -
Ali Bereyhi, Ralf R. Müller. (2018). Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing. IEEE SigPort. http://sigport.org/2502
Ali Bereyhi, Ralf R. Müller, 2018. Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing. Available at: http://sigport.org/2502.
Ali Bereyhi, Ralf R. Müller. (2018). "Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing." Web.
1. Ali Bereyhi, Ralf R. Müller. Maximum-A-Posteriori Signal Recovery with Prior Information: Applications to Compressive Sensing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2502

3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES


Given the high demand for automated systems for human action recognition, great efforts have been undertaken in recent decades to progress the field. In this paper, we present frameworks for single and multi-viewpoints action recognition based on Space-Time Volume (STV) of human silhouettes and 3D-Histogram of Oriented Gradient (3D-HOG) embedding.

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Authors:
Federico Angelini, Zeyu Fu, Sergio A. Velastin, Jonathon A. Chambers, Syed Mohsen Naqvi
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12 April 2018 - 4:29pm
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Poster

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[1] Federico Angelini, Zeyu Fu, Sergio A. Velastin, Jonathon A. Chambers, Syed Mohsen Naqvi, "3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2495. Accessed: Sep. 20, 2018.
@article{2495-18,
url = {http://sigport.org/2495},
author = {Federico Angelini; Zeyu Fu; Sergio A. Velastin; Jonathon A. Chambers; Syed Mohsen Naqvi },
publisher = {IEEE SigPort},
title = {3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES},
year = {2018} }
TY - EJOUR
T1 - 3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES
AU - Federico Angelini; Zeyu Fu; Sergio A. Velastin; Jonathon A. Chambers; Syed Mohsen Naqvi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2495
ER -
Federico Angelini, Zeyu Fu, Sergio A. Velastin, Jonathon A. Chambers, Syed Mohsen Naqvi. (2018). 3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES. IEEE SigPort. http://sigport.org/2495
Federico Angelini, Zeyu Fu, Sergio A. Velastin, Jonathon A. Chambers, Syed Mohsen Naqvi, 2018. 3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES. Available at: http://sigport.org/2495.
Federico Angelini, Zeyu Fu, Sergio A. Velastin, Jonathon A. Chambers, Syed Mohsen Naqvi. (2018). "3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES." Web.
1. Federico Angelini, Zeyu Fu, Sergio A. Velastin, Jonathon A. Chambers, Syed Mohsen Naqvi. 3D-HOG EMBEDDING FRAMEWORKS FOR SINGLE AND MULTI-VIEWPOINTS ACTION RECOGNITION BASED ON HUMAN SILHOUETTES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2495

ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS


The problem of sequential multiple hypothesis testing in a distributed sensor network is considered and two algorithms are proposed: the Consensus + Innovations Matrix Sequential Probability Ratio Test (CIMSPRT) for multiple simple hypotheses and the robust Least-Favorable-Density-CIMSPRT for hypotheses with uncertainties in the corresponding distributions. Simulations are performed to verify and evaluate the performance of both algorithms under different network conditions and noise contaminations.

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Authors:
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir
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12 April 2018 - 12:06pm
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Poster: ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS

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[1] Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir, "ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2427. Accessed: Sep. 20, 2018.
@article{2427-18,
url = {http://sigport.org/2427},
author = {Mark R. Leonard; Maximilian Stiefel; Michael Fauss; Abdelhak M. Zoubir },
publisher = {IEEE SigPort},
title = {ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS},
year = {2018} }
TY - EJOUR
T1 - ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS
AU - Mark R. Leonard; Maximilian Stiefel; Michael Fauss; Abdelhak M. Zoubir
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2427
ER -
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir. (2018). ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS. IEEE SigPort. http://sigport.org/2427
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir, 2018. ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS. Available at: http://sigport.org/2427.
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir. (2018). "ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS." Web.
1. Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir. ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2427

ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS


The power of particle filters in tracking the state of non-linear and non-Gaussian systems stems not only from their simple numerical implementation but also from their optimality and convergence properties. In particle filtering, the posterior distribution of the state is approximated by a discrete mass of samples, called particles, that stochastically evolve in time according to the dynamics of the model and the observations. Particle filters have been shown to converge almost surely toward the optimal filter as the number of particles increases.

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Authors:
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi
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9 November 2017 - 11:53am
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nesrine GLOBALSIP2018 - Copy.pdf

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[1] Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi, "ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2272. Accessed: Sep. 20, 2018.
@article{2272-17,
url = {http://sigport.org/2272},
author = {Nesrine Amor; Nidhal Carla Bouaynaya; Roman Shterenberg and Souad Chebbi },
publisher = {IEEE SigPort},
title = {ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS},
year = {2017} }
TY - EJOUR
T1 - ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS
AU - Nesrine Amor; Nidhal Carla Bouaynaya; Roman Shterenberg and Souad Chebbi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2272
ER -
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi. (2017). ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS. IEEE SigPort. http://sigport.org/2272
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi, 2017. ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS. Available at: http://sigport.org/2272.
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi. (2017). "ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS." Web.
1. Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi. ON THE CONVERGENCE OF CONSTRAINED PARTICLE FILTERS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2272

A watermarking technique to secure printed QR-Codes


The QR (Quick Response) code is a two-dimensional barcode, which was designed for storage information and high speed reading applications. Being cheap to produce and fast to read, it becomes actually a popular solution for product labeling.
Ones try to make QR code a solution against counterfeiting. We present a novel technique that permits to create a secure printed QR code which is robust

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Authors:
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier
Submitted On:
14 November 2017 - 9:39pm
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poster-final.pdf

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poster-final.pdf

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[1] Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier, "A watermarking technique to secure printed QR-Codes", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2266. Accessed: Sep. 20, 2018.
@article{2266-17,
url = {http://sigport.org/2266},
author = {Hoai Phuong Nguyen; Angès Delahaies; Florent Retraint; Dục Huy Nguyen; Marc Pic; Frédéric Morain-Nicolier },
publisher = {IEEE SigPort},
title = {A watermarking technique to secure printed QR-Codes},
year = {2017} }
TY - EJOUR
T1 - A watermarking technique to secure printed QR-Codes
AU - Hoai Phuong Nguyen; Angès Delahaies; Florent Retraint; Dục Huy Nguyen; Marc Pic; Frédéric Morain-Nicolier
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2266
ER -
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier. (2017). A watermarking technique to secure printed QR-Codes. IEEE SigPort. http://sigport.org/2266
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier, 2017. A watermarking technique to secure printed QR-Codes. Available at: http://sigport.org/2266.
Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier. (2017). "A watermarking technique to secure printed QR-Codes." Web.
1. Hoai Phuong Nguyen, Angès Delahaies, Florent Retraint, Dục Huy Nguyen, Marc Pic, Frédéric Morain-Nicolier. A watermarking technique to secure printed QR-Codes [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2266

COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION

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Authors:
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup
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9 November 2017 - 11:02am
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GlobalSIP17_PID1189_Final.pdf

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[1] Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup, "COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2264. Accessed: Sep. 20, 2018.
@article{2264-17,
url = {http://sigport.org/2264},
author = {Huynh Van Luong; Nikos Deligiannis; Jurgen Seiler; Soren Forchhammer; Andre Kaup },
publisher = {IEEE SigPort},
title = {COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION},
year = {2017} }
TY - EJOUR
T1 - COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION
AU - Huynh Van Luong; Nikos Deligiannis; Jurgen Seiler; Soren Forchhammer; Andre Kaup
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2264
ER -
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup. (2017). COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION. IEEE SigPort. http://sigport.org/2264
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup, 2017. COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION. Available at: http://sigport.org/2264.
Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup. (2017). "COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION." Web.
1. Huynh Van Luong, Nikos Deligiannis, Jurgen Seiler, Soren Forchhammer, Andre Kaup. COMPRESSIVE ONLINE ROBUST PRINCIPAL COMPONENT ANALYSIS WITH MULTIPLE PRIOR INFORMATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2264

Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation


The sequential analysis of the problem of joint signal detection and signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model is considered. The problem is posed as an optimization setup where the goal is to minimize the number of samples required to achieve the desired (i) type I and type II error probabilities and (ii) mean squared error performance.

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Authors:
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor
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13 March 2017 - 12:12pm
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poster1_icassp_2017_fauss.pdf

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[1] M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor, "Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1759. Accessed: Sep. 20, 2018.
@article{1759-17,
url = {http://sigport.org/1759},
author = {M. Fauß; K. G. Nagananda; A. M. Zoubir; H. V. Poor },
publisher = {IEEE SigPort},
title = {Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation},
year = {2017} }
TY - EJOUR
T1 - Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation
AU - M. Fauß; K. G. Nagananda; A. M. Zoubir; H. V. Poor
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1759
ER -
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor. (2017). Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation. IEEE SigPort. http://sigport.org/1759
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor, 2017. Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation. Available at: http://sigport.org/1759.
M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor. (2017). "Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation." Web.
1. M. Fauß, K. G. Nagananda, A. M. Zoubir, H. V. Poor. Sequential Joint Signal Detection and Signal-to-Noise Ratio Estimation [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1759

Robust Particle Filter by Dynamic Averaging of Multiple Noise Models


State filtering is a key problem in many signal processing applications. From a series of noisy measurement, one would like to estimate the state of some dynamic system. Existing techniques usually adopt a Gaussian noise assumption which may result in a major degradation in performance when the measurements are with the presence of outliers. A robust algorithm immune to the presence of outliers is desirable.

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11 March 2017 - 11:15am
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ICASSP_Liu_Bin.pdf

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[1] , "Robust Particle Filter by Dynamic Averaging of Multiple Noise Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1743. Accessed: Sep. 20, 2018.
@article{1743-17,
url = {http://sigport.org/1743},
author = { },
publisher = {IEEE SigPort},
title = {Robust Particle Filter by Dynamic Averaging of Multiple Noise Models},
year = {2017} }
TY - EJOUR
T1 - Robust Particle Filter by Dynamic Averaging of Multiple Noise Models
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1743
ER -
. (2017). Robust Particle Filter by Dynamic Averaging of Multiple Noise Models. IEEE SigPort. http://sigport.org/1743
, 2017. Robust Particle Filter by Dynamic Averaging of Multiple Noise Models. Available at: http://sigport.org/1743.
. (2017). "Robust Particle Filter by Dynamic Averaging of Multiple Noise Models." Web.
1. . Robust Particle Filter by Dynamic Averaging of Multiple Noise Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1743

Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction

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Authors:
Leonardo M. Millefiori, P. Braca and P. Willett
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6 March 2017 - 6:55pm
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poster-icassp-final.pdf

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[1] Leonardo M. Millefiori, P. Braca and P. Willett, "Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1670. Accessed: Sep. 20, 2018.
@article{1670-17,
url = {http://sigport.org/1670},
author = {Leonardo M. Millefiori; P. Braca and P. Willett },
publisher = {IEEE SigPort},
title = {Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction},
year = {2017} }
TY - EJOUR
T1 - Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction
AU - Leonardo M. Millefiori; P. Braca and P. Willett
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1670
ER -
Leonardo M. Millefiori, P. Braca and P. Willett. (2017). Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction. IEEE SigPort. http://sigport.org/1670
Leonardo M. Millefiori, P. Braca and P. Willett, 2017. Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction. Available at: http://sigport.org/1670.
Leonardo M. Millefiori, P. Braca and P. Willett. (2017). "Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction." Web.
1. Leonardo M. Millefiori, P. Braca and P. Willett. Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1670

AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering


One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.

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Authors:
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero
Submitted On:
5 March 2017 - 11:06pm
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ICASSP_AMOS_2017.pdf

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[1] Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1643. Accessed: Sep. 20, 2018.
@article{1643-17,
url = {http://sigport.org/1643},
author = {Pin-Yu Chen; Thibaut Gensollen; Alfred Hero },
publisher = {IEEE SigPort},
title = {AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering},
year = {2017} }
TY - EJOUR
T1 - AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
AU - Pin-Yu Chen; Thibaut Gensollen; Alfred Hero
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1643
ER -
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. IEEE SigPort. http://sigport.org/1643
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero, 2017. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering. Available at: http://sigport.org/1643.
Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. (2017). "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering." Web.
1. Pin-Yu Chen, Thibaut Gensollen, Alfred Hero. AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1643

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