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Emerging: Big Data

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

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Authors:
Dimitrios Berberidis, Georgios B. Giannakis
Submitted On:
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: Oct. 17, 2017.
@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: Oct. 17, 2017.
@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: Oct. 17, 2017.
@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 (351 downloads)

gsip_mc.pdf

PDF icon gsip_mc.pdf (231 downloads)

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

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gsip_mc.pdf

(231 downloads)

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[1] , "CRH: A Simple Benchmark Approach to Continuous Hashing", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/248. Accessed: Oct. 17, 2017.
@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: Oct. 17, 2017.
@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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