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Statistical Signal Processing

OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES


The paper considers the problem of estimating the covariance matrices of multiple classes in a low sample support condition, where the data dimensionality is comparable to, or larger than, the sample sizes of the available data sets. In such conditions, a common approach is to shrink the class sample covariance matrices (SCMs) towards the pooled SCM. The success of this approach hinges upon the ability to choose the optimal regularization parameter. Typically, a common regularization level is shared among the classes and determined via a procedure based on cross-validation.

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Authors:
Elias Raninen and Esa Ollila
Submitted On:
18 April 2018 - 11:40am
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[1] Elias Raninen and Esa Ollila, "OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2641. Accessed: Feb. 23, 2019.
@article{2641-18,
url = {http://sigport.org/2641},
author = {Elias Raninen and Esa Ollila },
publisher = {IEEE SigPort},
title = {OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES},
year = {2018} }
TY - EJOUR
T1 - OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES
AU - Elias Raninen and Esa Ollila
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2641
ER -
Elias Raninen and Esa Ollila. (2018). OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES. IEEE SigPort. http://sigport.org/2641
Elias Raninen and Esa Ollila, 2018. OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES. Available at: http://sigport.org/2641.
Elias Raninen and Esa Ollila. (2018). "OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES." Web.
1. Elias Raninen and Esa Ollila. OPTIMAL POOLING OF COVARIANCE MATRIX ESTIMATES ACROSS MULTIPLE CLASSES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2641

Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies


We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is specially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from ℓq,1 norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination.

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Authors:
Muhammad Naveed Tabassum and Esa Ollila
Submitted On:
13 April 2018 - 12:03am
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[1] Muhammad Naveed Tabassum and Esa Ollila, "Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2580. Accessed: Feb. 23, 2019.
@article{2580-18,
url = {http://sigport.org/2580},
author = {Muhammad Naveed Tabassum and Esa Ollila },
publisher = {IEEE SigPort},
title = {Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies},
year = {2018} }
TY - EJOUR
T1 - Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies
AU - Muhammad Naveed Tabassum and Esa Ollila
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2580
ER -
Muhammad Naveed Tabassum and Esa Ollila. (2018). Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies. IEEE SigPort. http://sigport.org/2580
Muhammad Naveed Tabassum and Esa Ollila, 2018. Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies. Available at: http://sigport.org/2580.
Muhammad Naveed Tabassum and Esa Ollila. (2018). "Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies." Web.
1. Muhammad Naveed Tabassum and Esa Ollila. Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2580

Change-Point Detection of Gaussian Graph Signals with Partial Information

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Authors:
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu
Submitted On:
13 April 2018 - 9:46pm
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[1] Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu, "Change-Point Detection of Gaussian Graph Signals with Partial Information", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2557. Accessed: Feb. 23, 2019.
@article{2557-18,
url = {http://sigport.org/2557},
author = {Yanxi Chen; Xianghui Mao; Dan Ling; Yuantao Gu },
publisher = {IEEE SigPort},
title = {Change-Point Detection of Gaussian Graph Signals with Partial Information},
year = {2018} }
TY - EJOUR
T1 - Change-Point Detection of Gaussian Graph Signals with Partial Information
AU - Yanxi Chen; Xianghui Mao; Dan Ling; Yuantao Gu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2557
ER -
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu. (2018). Change-Point Detection of Gaussian Graph Signals with Partial Information. IEEE SigPort. http://sigport.org/2557
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu, 2018. Change-Point Detection of Gaussian Graph Signals with Partial Information. Available at: http://sigport.org/2557.
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu. (2018). "Change-Point Detection of Gaussian Graph Signals with Partial Information." Web.
1. Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu. Change-Point Detection of Gaussian Graph Signals with Partial Information [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2557

Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information

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Authors:
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu
Submitted On:
18 April 2018 - 7:26pm
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[1] Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu, "Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2556. Accessed: Feb. 23, 2019.
@article{2556-18,
url = {http://sigport.org/2556},
author = {Yanxi Chen; Xianghui Mao; Dan Ling; Yuantao Gu },
publisher = {IEEE SigPort},
title = {Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information},
year = {2018} }
TY - EJOUR
T1 - Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information
AU - Yanxi Chen; Xianghui Mao; Dan Ling; Yuantao Gu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2556
ER -
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu. (2018). Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information. IEEE SigPort. http://sigport.org/2556
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu, 2018. Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information. Available at: http://sigport.org/2556.
Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu. (2018). "Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information." Web.
1. Yanxi Chen, Xianghui Mao, Dan Ling, Yuantao Gu. Slides: Change-Point Detection of Gaussian Graph Signals with Partial Information [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2556

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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[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: Feb. 23, 2019.
@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
Submitted On:
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: Feb. 23, 2019.
@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
Submitted On:
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: Feb. 23, 2019.
@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.

Paper Details

Authors:
Nesrine Amor, Nidhal Carla Bouaynaya, Roman Shterenberg and Souad Chebbi
Submitted On:
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: Feb. 23, 2019.
@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

Paper Details

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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[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: Feb. 23, 2019.
@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
Submitted On:
9 November 2017 - 11:02am
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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: Feb. 23, 2019.
@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

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