Sorry, you need to enable JavaScript to visit this website.

Graphical and kernel methods (MLR-GRKN)

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 (47 downloads)

Paper Details

Authors:
Zhen Li, Jingtian Bai, Weilian Zhou
Submitted On:
15 April 2018 - 7:37pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Li.pdf

(47 downloads)

Subscribe

[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. 21, 2018.
@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.

Paper Details

Authors:
Soobeom Jang, Seong-Eun Moon, Jong-Seok Lee
Submitted On:
13 April 2018 - 1:13am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Jang_ICASSP2018_3rd_180405.pdf

(72 downloads)

Subscribe

[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. 21, 2018.
@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

Paper Details

Authors:
Dimitris Berberidis, Georgios B. Giannakis
Submitted On:
11 November 2017 - 5:35pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster_globalsip2017.pdf

(246 downloads)

Subscribe

[1] Dimitris Berberidis, Georgios B. Giannakis, "Active Sampling for Graph-Aware Classification", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2308. Accessed: Aug. 21, 2018.
@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

Paper Details

Authors:
Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, and
Submitted On:
10 November 2017 - 2:16pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:

Document Files

Globalsip17_JianDu_CMU.pdf

(99 downloads)

Subscribe

[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. 21, 2018.
@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
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

globalsip17_slides.pdf

(90 downloads)

Subscribe

[1] Tianpei Xie, Sijia Liu, Alfred O. Hero III, "GlobalSip_2017", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2267. Accessed: Aug. 21, 2018.
@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

Paper Details

Authors:
Anh T. Pham, Raviv Raich, Xiaoli Z. Fern
Submitted On:
19 March 2016 - 8:25pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Poster_ICASSP.pdf

(389 downloads)

Subscribe

[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. 21, 2018.
@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 (721 downloads)

Paper Details

Authors:
Donald Donald
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Document Year:
Cite

Document Files

McCuan_PhdThesis.pdf

(721 downloads)

Subscribe

[1] Donald Donald, "Models for Spectral Clustering and Their Applications", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/564. Accessed: Aug. 21, 2018.
@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

Multigrid Eigensolvers for Image Segmentation


Spectral Image Segmentation

Presentation at LANL and UC Davis, 2009. Originally posted at http://math.ucdenver.edu/~aknyazev/research/conf/

LANL09.ppt

Office presentation icon LANL09.ppt (415 downloads)

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Document Year:
Cite

Document Files

LANL09.ppt

(415 downloads)

Subscribe

[1] , "Multigrid Eigensolvers for Image Segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/562. Accessed: Aug. 21, 2018.
@article{562-15,
url = {http://sigport.org/562},
author = { },
publisher = {IEEE SigPort},
title = {Multigrid Eigensolvers for Image Segmentation},
year = {2015} }
TY - EJOUR
T1 - Multigrid Eigensolvers for Image Segmentation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/562
ER -
. (2015). Multigrid Eigensolvers for Image Segmentation. IEEE SigPort. http://sigport.org/562
, 2015. Multigrid Eigensolvers for Image Segmentation. Available at: http://sigport.org/562.
. (2015). "Multigrid Eigensolvers for Image Segmentation." Web.
1. . Multigrid Eigensolvers for Image Segmentation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/562

Novel data clustering for microarrays and image segmentation


Spectral Clustering Eigenvalue Problem

We develop novel algorithms and software on parallel computers for data clustering of large datasets. We are interested in applying our approach, e.g., for analysis of large datasets of microarrays or tiling arrays in molecular biology and for segmentation of high resolution images.

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Document Year:
Cite

Document Files

camready-1031.ppt

(387 downloads)

Subscribe

[1] , "Novel data clustering for microarrays and image segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/561. Accessed: Aug. 21, 2018.
@article{561-15,
url = {http://sigport.org/561},
author = { },
publisher = {IEEE SigPort},
title = {Novel data clustering for microarrays and image segmentation},
year = {2015} }
TY - EJOUR
T1 - Novel data clustering for microarrays and image segmentation
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/561
ER -
. (2015). Novel data clustering for microarrays and image segmentation. IEEE SigPort. http://sigport.org/561
, 2015. Novel data clustering for microarrays and image segmentation. Available at: http://sigport.org/561.
. (2015). "Novel data clustering for microarrays and image segmentation." Web.
1. . Novel data clustering for microarrays and image segmentation [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/561

Edge-enhancing filters with negative weights


Edge-enhanced eigenvectors of the Laplacian with a negative weight

In [doi{10.1109/ICMEW.2014.6890711}], a~graph-based filtering of noisy images is performed by directly computing a projection of the image to be filtered onto a lower dimensional Krylov subspace of the graph Laplacian, constructed using non-negative graph weights determined by distances between image data corresponding to image pixels. We extend the construction of the graph Laplacian to the case, where some graph weights can be negative.

KGlobalSIP.pdf

PDF icon KGlobalSIP.pdf (453 downloads)

Paper Details

Authors:
Submitted On:
23 February 2016 - 1:44pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

KGlobalSIP.pdf

(453 downloads)

Subscribe

[1] , "Edge-enhancing filters with negative weights", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/503. Accessed: Aug. 21, 2018.
@article{503-15,
url = {http://sigport.org/503},
author = { },
publisher = {IEEE SigPort},
title = {Edge-enhancing filters with negative weights},
year = {2015} }
TY - EJOUR
T1 - Edge-enhancing filters with negative weights
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/503
ER -
. (2015). Edge-enhancing filters with negative weights. IEEE SigPort. http://sigport.org/503
, 2015. Edge-enhancing filters with negative weights. Available at: http://sigport.org/503.
. (2015). "Edge-enhancing filters with negative weights." Web.
1. . Edge-enhancing filters with negative weights [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/503

Pages