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Signal Processing Theory and Methods

Robust Matrix Completion via Alternating Projection


Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem.

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19 June 2017 - 11:39pm
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[1] , "Robust Matrix Completion via Alternating Projection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1798. Accessed: Jun. 24, 2017.
@article{1798-17,
url = {http://sigport.org/1798},
author = { },
publisher = {IEEE SigPort},
title = {Robust Matrix Completion via Alternating Projection},
year = {2017} }
TY - EJOUR
T1 - Robust Matrix Completion via Alternating Projection
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1798
ER -
. (2017). Robust Matrix Completion via Alternating Projection. IEEE SigPort. http://sigport.org/1798
, 2017. Robust Matrix Completion via Alternating Projection. Available at: http://sigport.org/1798.
. (2017). "Robust Matrix Completion via Alternating Projection." Web.
1. . Robust Matrix Completion via Alternating Projection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1798

PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING


Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propose a novel video-based multi-target tracking system that combines the particle probability hypothesis density (PHD) filter with discriminative group-structured dictionary learning.

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Authors:
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers
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22 March 2017 - 8:04am
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ICASSP2017-POSTER (1).pdf

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[1] Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers, "PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1780. Accessed: Jun. 24, 2017.
@article{1780-17,
url = {http://sigport.org/1780},
author = {Zeyu Fu; Pengming Feng; Syed Mohsen Naqvi; and Jonathon Chambers },
publisher = {IEEE SigPort},
title = {PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING},
year = {2017} }
TY - EJOUR
T1 - PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING
AU - Zeyu Fu; Pengming Feng; Syed Mohsen Naqvi; and Jonathon Chambers
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1780
ER -
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. (2017). PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING. IEEE SigPort. http://sigport.org/1780
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers, 2017. PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING. Available at: http://sigport.org/1780.
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. (2017). "PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING." Web.
1. Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1780

An M-Channel Critically Sampled Graph Filter Bank


We investigate an M-channel critically sampled filter bank for graph signals where each of the M filters is supported on a different subband of the graph Laplacian spectrum. We partition the graph vertices such that the mth set comprises a uniqueness set for signals supported on the mth subband. For analysis, the graph signal is filtered on each subband and downsampled on the corresponding set of vertices.

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Authors:
Yan Jin, David I Shuman
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8 March 2017 - 4:26pm
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Jin_ICASSP_2017_Prez_3066.pdf

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[1] Yan Jin, David I Shuman, "An M-Channel Critically Sampled Graph Filter Bank", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1711. Accessed: Jun. 24, 2017.
@article{1711-17,
url = {http://sigport.org/1711},
author = {Yan Jin; David I Shuman },
publisher = {IEEE SigPort},
title = {An M-Channel Critically Sampled Graph Filter Bank},
year = {2017} }
TY - EJOUR
T1 - An M-Channel Critically Sampled Graph Filter Bank
AU - Yan Jin; David I Shuman
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1711
ER -
Yan Jin, David I Shuman. (2017). An M-Channel Critically Sampled Graph Filter Bank. IEEE SigPort. http://sigport.org/1711
Yan Jin, David I Shuman, 2017. An M-Channel Critically Sampled Graph Filter Bank. Available at: http://sigport.org/1711.
Yan Jin, David I Shuman. (2017). "An M-Channel Critically Sampled Graph Filter Bank." Web.
1. Yan Jin, David I Shuman. An M-Channel Critically Sampled Graph Filter Bank [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1711

Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery


Historically, sparse methods and neural networks, particularly modern deep learning methods, have been relatively disparate areas. Sparse methods are typically used for signal enhancement, compression,and recovery, usually in an unsupervised framework, while neural networks commonly rely on a supervised training set.

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Authors:
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas
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8 March 2017 - 9:22am
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poster_icassp2017.pdf

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[1] Scott Wisdom, Thomas Powers, James Pitton, Les Atlas, "Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1706. Accessed: Jun. 24, 2017.
@article{1706-17,
url = {http://sigport.org/1706},
author = {Scott Wisdom; Thomas Powers; James Pitton; Les Atlas },
publisher = {IEEE SigPort},
title = {Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery},
year = {2017} }
TY - EJOUR
T1 - Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery
AU - Scott Wisdom; Thomas Powers; James Pitton; Les Atlas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1706
ER -
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas. (2017). Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery. IEEE SigPort. http://sigport.org/1706
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas, 2017. Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery. Available at: http://sigport.org/1706.
Scott Wisdom, Thomas Powers, James Pitton, Les Atlas. (2017). "Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery." Web.
1. Scott Wisdom, Thomas Powers, James Pitton, Les Atlas. Building Recurrent Networks by Unfolding Iterative Thresholding for Sequential Sparse Recovery [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1706

NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF

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7 March 2017 - 2:12pm
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posterICASSP_Gordana.pdf

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[1] , "NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1694. Accessed: Jun. 24, 2017.
@article{1694-17,
url = {http://sigport.org/1694},
author = { },
publisher = {IEEE SigPort},
title = {NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF},
year = {2017} }
TY - EJOUR
T1 - NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1694
ER -
. (2017). NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF. IEEE SigPort. http://sigport.org/1694
, 2017. NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF. Available at: http://sigport.org/1694.
. (2017). "NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF." Web.
1. . NEW ASYMPTOTIC PROPERTIES FOR THE ROBUST ANMF [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1694

SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY

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Authors:
Odysseas Pappas. Alin Achim. Dave Bull
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28 February 2017 - 6:49am
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icassp_2017_posterdraft.pptx

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[1] Odysseas Pappas. Alin Achim. Dave Bull, "SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1503. Accessed: Jun. 24, 2017.
@article{1503-17,
url = {http://sigport.org/1503},
author = {Odysseas Pappas. Alin Achim. Dave Bull },
publisher = {IEEE SigPort},
title = {SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY},
year = {2017} }
TY - EJOUR
T1 - SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY
AU - Odysseas Pappas. Alin Achim. Dave Bull
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1503
ER -
Odysseas Pappas. Alin Achim. Dave Bull. (2017). SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY. IEEE SigPort. http://sigport.org/1503
Odysseas Pappas. Alin Achim. Dave Bull, 2017. SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY. Available at: http://sigport.org/1503.
Odysseas Pappas. Alin Achim. Dave Bull. (2017). "SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY." Web.
1. Odysseas Pappas. Alin Achim. Dave Bull. SUPERPIXEL-GUIDED CFAR DETECTION OF SHIPS AT SEA IN SAR IMAGERY [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1503

Online Empirical Mode Decomposition


The success of Empirical Mode Decomposition (EMD) resides in its practical approach to dissect non-stationary data. EMD repetitively goes through the entire data span to iteratively extract Intrinsic Mode Functions (IMFs). This approach, however, is not suitable for data stream as the entire data set has to be reconsidered every time a new point is added. To overcome this, we propose Online EMD, an algorithm that extracts IMFs on the fly.

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Authors:
Romain Fontugne, Pierre Borgnat, Patrick Flandrin
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28 February 2017 - 4:29am
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icassp17_poster_onlineEMD.pdf

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[1] Romain Fontugne, Pierre Borgnat, Patrick Flandrin, "Online Empirical Mode Decomposition", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1491. Accessed: Jun. 24, 2017.
@article{1491-17,
url = {http://sigport.org/1491},
author = {Romain Fontugne; Pierre Borgnat; Patrick Flandrin },
publisher = {IEEE SigPort},
title = {Online Empirical Mode Decomposition},
year = {2017} }
TY - EJOUR
T1 - Online Empirical Mode Decomposition
AU - Romain Fontugne; Pierre Borgnat; Patrick Flandrin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1491
ER -
Romain Fontugne, Pierre Borgnat, Patrick Flandrin. (2017). Online Empirical Mode Decomposition. IEEE SigPort. http://sigport.org/1491
Romain Fontugne, Pierre Borgnat, Patrick Flandrin, 2017. Online Empirical Mode Decomposition. Available at: http://sigport.org/1491.
Romain Fontugne, Pierre Borgnat, Patrick Flandrin. (2017). "Online Empirical Mode Decomposition." Web.
1. Romain Fontugne, Pierre Borgnat, Patrick Flandrin. Online Empirical Mode Decomposition [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1491

Linear Systems on Graphs

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Authors:
Oguzhan Teke, Palghat P. Vaidyanathan
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11 December 2016 - 12:04am
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linearSystems_globalsip_presentation.pdf

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[1] Oguzhan Teke, Palghat P. Vaidyanathan, "Linear Systems on Graphs", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1437. Accessed: Jun. 24, 2017.
@article{1437-16,
url = {http://sigport.org/1437},
author = {Oguzhan Teke; Palghat P. Vaidyanathan },
publisher = {IEEE SigPort},
title = {Linear Systems on Graphs},
year = {2016} }
TY - EJOUR
T1 - Linear Systems on Graphs
AU - Oguzhan Teke; Palghat P. Vaidyanathan
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1437
ER -
Oguzhan Teke, Palghat P. Vaidyanathan. (2016). Linear Systems on Graphs. IEEE SigPort. http://sigport.org/1437
Oguzhan Teke, Palghat P. Vaidyanathan, 2016. Linear Systems on Graphs. Available at: http://sigport.org/1437.
Oguzhan Teke, Palghat P. Vaidyanathan. (2016). "Linear Systems on Graphs." Web.
1. Oguzhan Teke, Palghat P. Vaidyanathan. Linear Systems on Graphs [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1437

Tracking Time-Vertex Propagation using Dynamic Graph Wavelets


Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process.

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Authors:
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud
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8 December 2016 - 5:01pm
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globalsip_grassi.pdf

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[1] Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud, "Tracking Time-Vertex Propagation using Dynamic Graph Wavelets", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1428. Accessed: Jun. 24, 2017.
@article{1428-16,
url = {http://sigport.org/1428},
author = {Francesco Grassi; Nathanael Perraudin; Benjamin Ricaud },
publisher = {IEEE SigPort},
title = {Tracking Time-Vertex Propagation using Dynamic Graph Wavelets},
year = {2016} }
TY - EJOUR
T1 - Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
AU - Francesco Grassi; Nathanael Perraudin; Benjamin Ricaud
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1428
ER -
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud. (2016). Tracking Time-Vertex Propagation using Dynamic Graph Wavelets. IEEE SigPort. http://sigport.org/1428
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud, 2016. Tracking Time-Vertex Propagation using Dynamic Graph Wavelets. Available at: http://sigport.org/1428.
Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud. (2016). "Tracking Time-Vertex Propagation using Dynamic Graph Wavelets." Web.
1. Francesco Grassi, Nathanael Perraudin, Benjamin Ricaud. Tracking Time-Vertex Propagation using Dynamic Graph Wavelets [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1428

Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems


Sampling and reconstruction of bandlimited graph signals have well-appreciated merits for dimensionality reduction, affordable storage, and online processing of streaming network data. However, these parsimonious signals are oftentimes encountered with high-dimensional linear inverse problems. Hence, interest shifts from reconstructing the signal itself towards instead approximating the input to a prescribed linear operator efficiently.

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Authors:
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro
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8 December 2016 - 3:51pm
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sketching-globalsip16-presentation.pdf

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[1] Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1415. Accessed: Jun. 24, 2017.
@article{1415-16,
url = {http://sigport.org/1415},
author = {Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems},
year = {2016} }
TY - EJOUR
T1 - Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems
AU - Fernando Gama; Antonio G. Marques; Gonzalo Mateos; Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1415
ER -
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. IEEE SigPort. http://sigport.org/1415
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro, 2016. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems. Available at: http://sigport.org/1415.
Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. (2016). "Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems." Web.
1. Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro. Rethinking Sketching as Sampling: Efficient Approximate Solution to Linear Inverse Problems [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1415

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