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Emerging: Big Data

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.

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23 February 2016 - 1:44pm
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camready-1031.ppt

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[1] , "Novel data clustering for microarrays and image segmentation", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/561. Accessed: May. 25, 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

Social Media Analytics for Crisis Response


Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response.

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23 February 2016 - 1:44pm
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Shamanth_thesis.pdf

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[1] , "Social Media Analytics for Crisis Response", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/541. Accessed: May. 25, 2018.
@article{541-15,
url = {http://sigport.org/541},
author = { },
publisher = {IEEE SigPort},
title = {Social Media Analytics for Crisis Response},
year = {2015} }
TY - EJOUR
T1 - Social Media Analytics for Crisis Response
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/541
ER -
. (2015). Social Media Analytics for Crisis Response. IEEE SigPort. http://sigport.org/541
, 2015. Social Media Analytics for Crisis Response. Available at: http://sigport.org/541.
. (2015). "Social Media Analytics for Crisis Response." Web.
1. . Social Media Analytics for Crisis Response [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/541

Social Media Analytics for Crisis Response


Crises and situations of mass emergency such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally this process has been restricted to the information collected by first responders on the ground in the affected region or official agencies such as local governments involved in the response.

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23 February 2016 - 1:44pm
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Shamanth_Kumar_Thesis_slides_2015.pptx

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[1] , "Social Media Analytics for Crisis Response", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/539. Accessed: May. 25, 2018.
@article{539-15,
url = {http://sigport.org/539},
author = { },
publisher = {IEEE SigPort},
title = {Social Media Analytics for Crisis Response},
year = {2015} }
TY - EJOUR
T1 - Social Media Analytics for Crisis Response
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/539
ER -
. (2015). Social Media Analytics for Crisis Response. IEEE SigPort. http://sigport.org/539
, 2015. Social Media Analytics for Crisis Response. Available at: http://sigport.org/539.
. (2015). "Social Media Analytics for Crisis Response." Web.
1. . Social Media Analytics for Crisis Response [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/539

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 (393 downloads)

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KGlobalSIP.pdf

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[1] , "Edge-enhancing filters with negative weights", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/503. Accessed: May. 25, 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

Kernel-based low-rank feature extraction on a budget for Big data streams

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Authors:
Dimitrios Berberidis, Georgios B. Giannakis
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23 February 2016 - 1:44pm
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Globalsip2015.pdf

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[1] Dimitrios Berberidis, Georgios B. Giannakis, "Kernel-based low-rank feature extraction on a budget for Big data streams", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/434. Accessed: May. 25, 2018.
@article{434-15,
url = {http://sigport.org/434},
author = {Dimitrios Berberidis; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Kernel-based low-rank feature extraction on a budget for Big data streams},
year = {2015} }
TY - EJOUR
T1 - Kernel-based low-rank feature extraction on a budget for Big data streams
AU - Dimitrios Berberidis; Georgios B. Giannakis
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/434
ER -
Dimitrios Berberidis, Georgios B. Giannakis. (2015). Kernel-based low-rank feature extraction on a budget for Big data streams. IEEE SigPort. http://sigport.org/434
Dimitrios Berberidis, Georgios B. Giannakis, 2015. Kernel-based low-rank feature extraction on a budget for Big data streams. Available at: http://sigport.org/434.
Dimitrios Berberidis, Georgios B. Giannakis. (2015). "Kernel-based low-rank feature extraction on a budget for Big data streams." Web.
1. Dimitrios Berberidis, Georgios B. Giannakis. Kernel-based low-rank feature extraction on a budget for Big data streams [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/434

Guided Signal Reconstruction with Application to Image Magnification


Reconstruction Set

We propose signal reconstruction algorithms which utilize a guiding subspace that represents desired properties of reconstructed signals. Optimal reconstructed signals are shown to belong to a convex bounded set, called the ``reconstruction'' set. Iterative reconstruction algorithms, based on conjugate gradient methods, are developed to approximate optimal reconstructions with low memory and computational costs. Effectiveness of the proposed method is demonstrated with an application to image magnification.

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Authors:
Akshay Gadde, Hassan Mansour, Dong Tian
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23 February 2016 - 1:44pm
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globalsip-15-slides-v2.pdf

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[1] Akshay Gadde, Hassan Mansour, Dong Tian, "Guided Signal Reconstruction with Application to Image Magnification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/384. Accessed: May. 25, 2018.
@article{384-15,
url = {http://sigport.org/384},
author = {Akshay Gadde; Hassan Mansour; Dong Tian },
publisher = {IEEE SigPort},
title = {Guided Signal Reconstruction with Application to Image Magnification},
year = {2015} }
TY - EJOUR
T1 - Guided Signal Reconstruction with Application to Image Magnification
AU - Akshay Gadde; Hassan Mansour; Dong Tian
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/384
ER -
Akshay Gadde, Hassan Mansour, Dong Tian. (2015). Guided Signal Reconstruction with Application to Image Magnification. IEEE SigPort. http://sigport.org/384
Akshay Gadde, Hassan Mansour, Dong Tian, 2015. Guided Signal Reconstruction with Application to Image Magnification. Available at: http://sigport.org/384.
Akshay Gadde, Hassan Mansour, Dong Tian. (2015). "Guided Signal Reconstruction with Application to Image Magnification." Web.
1. Akshay Gadde, Hassan Mansour, Dong Tian. Guided Signal Reconstruction with Application to Image Magnification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/384

Sparse Phase Retrieval Using Partial Nested Fourier Samplers

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Authors:
Piya Pal
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23 February 2016 - 1:44pm
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presentation.pdf

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[1] Piya Pal, "Sparse Phase Retrieval Using Partial Nested Fourier Samplers", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/276. Accessed: May. 25, 2018.
@article{276-15,
url = {http://sigport.org/276},
author = {Piya Pal },
publisher = {IEEE SigPort},
title = {Sparse Phase Retrieval Using Partial Nested Fourier Samplers},
year = {2015} }
TY - EJOUR
T1 - Sparse Phase Retrieval Using Partial Nested Fourier Samplers
AU - Piya Pal
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/276
ER -
Piya Pal. (2015). Sparse Phase Retrieval Using Partial Nested Fourier Samplers. IEEE SigPort. http://sigport.org/276
Piya Pal, 2015. Sparse Phase Retrieval Using Partial Nested Fourier Samplers. Available at: http://sigport.org/276.
Piya Pal. (2015). "Sparse Phase Retrieval Using Partial Nested Fourier Samplers." Web.
1. Piya Pal. Sparse Phase Retrieval Using Partial Nested Fourier Samplers [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/276

CRH: A Simple Benchmark Approach to Continuous Hashing


gsip_mc.pdf

PDF icon gsip_mc.pdf (458 downloads)

gsip_mc.pdf

PDF icon gsip_mc.pdf (336 downloads)

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Authors:
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23 February 2016 - 1:43pm
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gsip_mc.pdf

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gsip_mc.pdf

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[1] , "CRH: A Simple Benchmark Approach to Continuous Hashing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/248. Accessed: May. 25, 2018.
@article{248-15,
url = {http://sigport.org/248},
author = { },
publisher = {IEEE SigPort},
title = {CRH: A Simple Benchmark Approach to Continuous Hashing},
year = {2015} }
TY - EJOUR
T1 - CRH: A Simple Benchmark Approach to Continuous Hashing
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/248
ER -
. (2015). CRH: A Simple Benchmark Approach to Continuous Hashing. IEEE SigPort. http://sigport.org/248
, 2015. CRH: A Simple Benchmark Approach to Continuous Hashing. Available at: http://sigport.org/248.
. (2015). "CRH: A Simple Benchmark Approach to Continuous Hashing." Web.
1. . CRH: A Simple Benchmark Approach to Continuous Hashing [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/248

Decision Learning in Data Science


With the increasing ubiquity and power of mobile devices, as well as the prevalence of social systems, more and more

Paper Details

Authors:
Chunxiao Jiang, Chih-Yu Wang, Yang Gao
Submitted On:
23 February 2016 - 1:43pm
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manuscript_0305.pdf

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[1] Chunxiao Jiang, Chih-Yu Wang, Yang Gao, "Decision Learning in Data Science", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/204. Accessed: May. 25, 2018.
@article{204-15,
url = {http://sigport.org/204},
author = {Chunxiao Jiang; Chih-Yu Wang; Yang Gao },
publisher = {IEEE SigPort},
title = {Decision Learning in Data Science},
year = {2015} }
TY - EJOUR
T1 - Decision Learning in Data Science
AU - Chunxiao Jiang; Chih-Yu Wang; Yang Gao
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/204
ER -
Chunxiao Jiang, Chih-Yu Wang, Yang Gao. (2015). Decision Learning in Data Science. IEEE SigPort. http://sigport.org/204
Chunxiao Jiang, Chih-Yu Wang, Yang Gao, 2015. Decision Learning in Data Science. Available at: http://sigport.org/204.
Chunxiao Jiang, Chih-Yu Wang, Yang Gao. (2015). "Decision Learning in Data Science." Web.
1. Chunxiao Jiang, Chih-Yu Wang, Yang Gao. Decision Learning in Data Science [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/204

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