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Graphical and kernel methods (MLR-GRKN)

Graph Filtering with Multiple Shift Matrices

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Authors:
CIHAN TEPEDELENLIOGLU, ANDREAS SPANIAS
Submitted On:
8 May 2019 - 5:54pm
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Jie_Fan_ICASSP_Poster(GraphClassification).pdf

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[1] CIHAN TEPEDELENLIOGLU, ANDREAS SPANIAS, "Graph Filtering with Multiple Shift Matrices", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4136. Accessed: Aug. 19, 2019.
@article{4136-19,
url = {http://sigport.org/4136},
author = {CIHAN TEPEDELENLIOGLU; ANDREAS SPANIAS },
publisher = {IEEE SigPort},
title = {Graph Filtering with Multiple Shift Matrices},
year = {2019} }
TY - EJOUR
T1 - Graph Filtering with Multiple Shift Matrices
AU - CIHAN TEPEDELENLIOGLU; ANDREAS SPANIAS
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4136
ER -
CIHAN TEPEDELENLIOGLU, ANDREAS SPANIAS. (2019). Graph Filtering with Multiple Shift Matrices. IEEE SigPort. http://sigport.org/4136
CIHAN TEPEDELENLIOGLU, ANDREAS SPANIAS, 2019. Graph Filtering with Multiple Shift Matrices. Available at: http://sigport.org/4136.
CIHAN TEPEDELENLIOGLU, ANDREAS SPANIAS. (2019). "Graph Filtering with Multiple Shift Matrices." Web.
1. CIHAN TEPEDELENLIOGLU, ANDREAS SPANIAS. Graph Filtering with Multiple Shift Matrices [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4136

Revisiting and improving semi-supervised learning: a large dimensional approach

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Authors:
Romain COUILLET
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8 May 2019 - 1:21pm
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poster_ICASSP_SSL_2.pdf

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[1] Romain COUILLET, "Revisiting and improving semi-supervised learning: a large dimensional approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4125. Accessed: Aug. 19, 2019.
@article{4125-19,
url = {http://sigport.org/4125},
author = {Romain COUILLET },
publisher = {IEEE SigPort},
title = {Revisiting and improving semi-supervised learning: a large dimensional approach},
year = {2019} }
TY - EJOUR
T1 - Revisiting and improving semi-supervised learning: a large dimensional approach
AU - Romain COUILLET
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4125
ER -
Romain COUILLET. (2019). Revisiting and improving semi-supervised learning: a large dimensional approach. IEEE SigPort. http://sigport.org/4125
Romain COUILLET, 2019. Revisiting and improving semi-supervised learning: a large dimensional approach. Available at: http://sigport.org/4125.
Romain COUILLET. (2019). "Revisiting and improving semi-supervised learning: a large dimensional approach." Web.
1. Romain COUILLET. Revisiting and improving semi-supervised learning: a large dimensional approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4125

POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS


Electroencephalography (EEG) has been widely used in human brain research. Several techniques in EEG relies on analyzing the topographical distribution of the data. One of the most common analysis is EEG microstate (EEG-ms). EEG-ms reflects the stable topographical representation of EEG signal lasting a few dozen milliseconds. EEG-ms were associated with resting state fMRI networks and were associated with mental processes and abnormalities.

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Authors:
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka
Submitted On:
23 November 2018 - 8:20pm
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A presentation for polarity invariant transformation for EEG microstates analysis

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[1] Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka , "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3761. Accessed: Aug. 19, 2019.
@article{3761-18,
url = {http://sigport.org/3761},
author = {Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka },
publisher = {IEEE SigPort},
title = {POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS
AU - Ahmad Mayeli; Hazem Refai; Martin Paulus; Jerzy Bodurka
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3761
ER -
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . (2018). POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS. IEEE SigPort. http://sigport.org/3761
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka , 2018. POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS. Available at: http://sigport.org/3761.
Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . (2018). "POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS." Web.
1. Ahmad Mayeli, Hazem Refai, Martin Paulus, Jerzy Bodurka . POLARITY INVARIANT TRANSFORMATION FOR EEG MICROSTATES ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3761

LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO


We develop a new method called Discriminated Hub Graphical Lasso (DHGL) based on Hub Graphical Lasso (HGL) by providing the prior information of hubs. We apply this new method in two situations: with known hubs and without known hubs. Then we compare DHGL with HGL using several measures of performance. When some hubs are known, we can always estimate the precision matrix better via DHGL than HGL.

Li.pdf

PDF icon Li.pdf (131)

Paper Details

Authors:
Zhen Li, Jingtian Bai, Weilian Zhou
Submitted On:
15 April 2018 - 7:37pm
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Li.pdf

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[1] Zhen Li, Jingtian Bai, Weilian Zhou, "LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2881. Accessed: Aug. 19, 2019.
@article{2881-18,
url = {http://sigport.org/2881},
author = {Zhen Li; Jingtian Bai; Weilian Zhou },
publisher = {IEEE SigPort},
title = {LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO},
year = {2018} }
TY - EJOUR
T1 - LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO
AU - Zhen Li; Jingtian Bai; Weilian Zhou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2881
ER -
Zhen Li, Jingtian Bai, Weilian Zhou. (2018). LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO. IEEE SigPort. http://sigport.org/2881
Zhen Li, Jingtian Bai, Weilian Zhou, 2018. LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO. Available at: http://sigport.org/2881.
Zhen Li, Jingtian Bai, Weilian Zhou. (2018). "LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO." Web.
1. Zhen Li, Jingtian Bai, Weilian Zhou. LEARNING GAUSSIAN GRAPHICAL MODELS USING DISCRIMINATED HUB GRAPHICAL LASSO [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2881

EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK


This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.

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Authors:
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
Submitted On:
13 April 2018 - 1:13am
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Jang_ICASSP2018_3rd_180405.pdf

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[1] Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee, "EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2604. Accessed: Aug. 19, 2019.
@article{2604-18,
url = {http://sigport.org/2604},
author = {Soobeom Jang; Seong-Eun Moon; Jong-Seok Lee },
publisher = {IEEE SigPort},
title = {EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK},
year = {2018} }
TY - EJOUR
T1 - EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK
AU - Soobeom Jang; Seong-Eun Moon; Jong-Seok Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2604
ER -
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee. (2018). EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK. IEEE SigPort. http://sigport.org/2604
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee, 2018. EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK. Available at: http://sigport.org/2604.
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee. (2018). "EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK." Web.
1. Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee. EEG-BASED VIDEO IDENTIFICATION USING GRAPH SIGNAL MODELING AND GRAPH CONVOLUTIONAL NEURAL NETWORK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2604

Active Sampling for Graph-Aware Classification

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Authors:
Dimitris Berberidis, Georgios B. Giannakis
Submitted On:
11 November 2017 - 5:35pm
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poster_globalsip2017.pdf

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[1] Dimitris Berberidis, Georgios B. Giannakis, "Active Sampling for Graph-Aware Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2308. Accessed: Aug. 19, 2019.
@article{2308-17,
url = {http://sigport.org/2308},
author = {Dimitris Berberidis; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Active Sampling for Graph-Aware Classification},
year = {2017} }
TY - EJOUR
T1 - Active Sampling for Graph-Aware Classification
AU - Dimitris Berberidis; Georgios B. Giannakis
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2308
ER -
Dimitris Berberidis, Georgios B. Giannakis. (2017). Active Sampling for Graph-Aware Classification. IEEE SigPort. http://sigport.org/2308
Dimitris Berberidis, Georgios B. Giannakis, 2017. Active Sampling for Graph-Aware Classification. Available at: http://sigport.org/2308.
Dimitris Berberidis, Georgios B. Giannakis. (2017). "Active Sampling for Graph-Aware Classification." Web.
1. Dimitris Berberidis, Georgios B. Giannakis. Active Sampling for Graph-Aware Classification [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2308

Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models

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Authors:
Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and
Submitted On:
10 November 2017 - 2:16pm
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Globalsip17_JianDu_CMU.pdf

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[1] Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and , "Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2298. Accessed: Aug. 19, 2019.
@article{2298-17,
url = {http://sigport.org/2298},
author = {Jian Du; Shaodan Ma; Yik-Chung Wu; Soummya Kar; and },
publisher = {IEEE SigPort},
title = {Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models},
year = {2017} }
TY - EJOUR
T1 - Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models
AU - Jian Du; Shaodan Ma; Yik-Chung Wu; Soummya Kar; and
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2298
ER -
Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and . (2017). Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models. IEEE SigPort. http://sigport.org/2298
Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and , 2017. Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models. Available at: http://sigport.org/2298.
Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and . (2017). "Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models." Web.
1. Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and . Convergence Analysis of Belief Propagation for Pairwise Linear Gaussian Models [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2298

GlobalSip_2017


Consider a social network where only a few nodes (agents)
have meaningful interactions in the sense that the conditional
dependency graph over node attribute variables (behaviors)
is sparse. A company that can only observe the interactions
between its own customers will generally not be able to ac-
curately estimate its customers’ dependency subgraph: it is
blinded to any external interactions of its customers and this
blindness creates false edges in its subgraph. In this paper
we address the semiblind scenario where the company has

Paper Details

Authors:
Tianpei Xie, Sijia Liu, Alfred O. Hero III
Submitted On:
9 November 2017 - 11:30am
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globalsip17_slides.pdf

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[1] Tianpei Xie, Sijia Liu, Alfred O. Hero III, "GlobalSip_2017", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2267. Accessed: Aug. 19, 2019.
@article{2267-17,
url = {http://sigport.org/2267},
author = {Tianpei Xie; Sijia Liu; Alfred O. Hero III },
publisher = {IEEE SigPort},
title = {GlobalSip_2017},
year = {2017} }
TY - EJOUR
T1 - GlobalSip_2017
AU - Tianpei Xie; Sijia Liu; Alfred O. Hero III
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2267
ER -
Tianpei Xie, Sijia Liu, Alfred O. Hero III. (2017). GlobalSip_2017. IEEE SigPort. http://sigport.org/2267
Tianpei Xie, Sijia Liu, Alfred O. Hero III, 2017. GlobalSip_2017. Available at: http://sigport.org/2267.
Tianpei Xie, Sijia Liu, Alfred O. Hero III. (2017). "GlobalSip_2017." Web.
1. Tianpei Xie, Sijia Liu, Alfred O. Hero III. GlobalSip_2017 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2267

Semi-supervised learning in the presence of novel class instances

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Authors:
Anh T. Pham, Raviv Raich, Xiaoli Z. Fern
Submitted On:
19 March 2016 - 8:25pm
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Poster_ICASSP.pdf

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[1] Anh T. Pham, Raviv Raich, Xiaoli Z. Fern, "Semi-supervised learning in the presence of novel class instances", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/838. Accessed: Aug. 19, 2019.
@article{838-16,
url = {http://sigport.org/838},
author = {Anh T. Pham; Raviv Raich; Xiaoli Z. Fern },
publisher = {IEEE SigPort},
title = {Semi-supervised learning in the presence of novel class instances},
year = {2016} }
TY - EJOUR
T1 - Semi-supervised learning in the presence of novel class instances
AU - Anh T. Pham; Raviv Raich; Xiaoli Z. Fern
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/838
ER -
Anh T. Pham, Raviv Raich, Xiaoli Z. Fern. (2016). Semi-supervised learning in the presence of novel class instances. IEEE SigPort. http://sigport.org/838
Anh T. Pham, Raviv Raich, Xiaoli Z. Fern, 2016. Semi-supervised learning in the presence of novel class instances. Available at: http://sigport.org/838.
Anh T. Pham, Raviv Raich, Xiaoli Z. Fern. (2016). "Semi-supervised learning in the presence of novel class instances." Web.
1. Anh T. Pham, Raviv Raich, Xiaoli Z. Fern. Semi-supervised learning in the presence of novel class instances [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/838

Models for Spectral Clustering and Their Applications


Microarray Spectral Clustering.

Ph.D. Thesis by Donald McCuan (advisor Andrew Knyazev), Department of Mathematical and Statistical Sciences, University of Colorado Denver, 2012, originally posted at http://math.ucdenver.edu/theses/McCuan_PhdThesis.pdf (875)

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Authors:
Donald Donald
Submitted On:
23 February 2016 - 1:44pm
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McCuan_PhdThesis.pdf

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[1] Donald Donald, "Models for Spectral Clustering and Their Applications", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/564. Accessed: Aug. 19, 2019.
@article{564-15,
url = {http://sigport.org/564},
author = {Donald Donald },
publisher = {IEEE SigPort},
title = {Models for Spectral Clustering and Their Applications},
year = {2015} }
TY - EJOUR
T1 - Models for Spectral Clustering and Their Applications
AU - Donald Donald
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/564
ER -
Donald Donald. (2015). Models for Spectral Clustering and Their Applications. IEEE SigPort. http://sigport.org/564
Donald Donald, 2015. Models for Spectral Clustering and Their Applications. Available at: http://sigport.org/564.
Donald Donald. (2015). "Models for Spectral Clustering and Their Applications." Web.
1. Donald Donald. Models for Spectral Clustering and Their Applications [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/564

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