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Signal and System Modeling, Representation and Estimation

Global Optimality in Inductive Matrix Completion


Inductive matrix completion (IMC) is a model for incorporating side information in form of “features” of the row and column entities of an unknown matrix in the matrix completion problem. As side information, features can substantially reduce the number of observed entries required for reconstructing an unknown matrix from its given entries. The IMC problem can be formulated as a low-rank matrix recovery problem where the observed entries are seen as measurements of a smaller matrix that models the interaction between the column and row features.

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Authors:
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela
Submitted On:
1 May 2018 - 11:04pm
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ICASSP2018.pdf

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[1] Mohsen Ghassemi, Anand D. Sarwate, Naveen goela, "Global Optimality in Inductive Matrix Completion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3171. Accessed: May. 23, 2018.
@article{3171-18,
url = {http://sigport.org/3171},
author = {Mohsen Ghassemi; Anand D. Sarwate; Naveen goela },
publisher = {IEEE SigPort},
title = {Global Optimality in Inductive Matrix Completion},
year = {2018} }
TY - EJOUR
T1 - Global Optimality in Inductive Matrix Completion
AU - Mohsen Ghassemi; Anand D. Sarwate; Naveen goela
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3171
ER -
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela. (2018). Global Optimality in Inductive Matrix Completion. IEEE SigPort. http://sigport.org/3171
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela, 2018. Global Optimality in Inductive Matrix Completion. Available at: http://sigport.org/3171.
Mohsen Ghassemi, Anand D. Sarwate, Naveen goela. (2018). "Global Optimality in Inductive Matrix Completion." Web.
1. Mohsen Ghassemi, Anand D. Sarwate, Naveen goela. Global Optimality in Inductive Matrix Completion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3171

ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements

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20 April 2018 - 4:30pm
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ICASSP_MW.pdf

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[1] , "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3113. Accessed: May. 23, 2018.
@article{3113-18,
url = {http://sigport.org/3113},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements},
year = {2018} }
TY - EJOUR
T1 - ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3113
ER -
. (2018). ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. IEEE SigPort. http://sigport.org/3113
, 2018. ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements. Available at: http://sigport.org/3113.
. (2018). "ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements." Web.
1. . ICASSP2018_Dynamic Matrix Recovery from Partially Observed and Erroneous Measurements [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3113

Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers

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Authors:
Pramod Varshney
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20 April 2018 - 1:17am
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icassp-2018_QLi.pdf

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[1] Pramod Varshney, "Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3076. Accessed: May. 23, 2018.
@article{3076-18,
url = {http://sigport.org/3076},
author = {Pramod Varshney },
publisher = {IEEE SigPort},
title = {Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers},
year = {2018} }
TY - EJOUR
T1 - Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers
AU - Pramod Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3076
ER -
Pramod Varshney. (2018). Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers. IEEE SigPort. http://sigport.org/3076
Pramod Varshney, 2018. Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers. Available at: http://sigport.org/3076.
Pramod Varshney. (2018). "Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers." Web.
1. Pramod Varshney. Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3076

PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA

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Authors:
Sandeep Kumar, Daniel P. Palomar
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20 April 2018 - 1:08am
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poster_ICASSP2018.pdf

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[1] Sandeep Kumar, Daniel P. Palomar, "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3073. Accessed: May. 23, 2018.
@article{3073-18,
url = {http://sigport.org/3073},
author = {Sandeep Kumar; Daniel P. Palomar },
publisher = {IEEE SigPort},
title = {PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA},
year = {2018} }
TY - EJOUR
T1 - PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA
AU - Sandeep Kumar; Daniel P. Palomar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3073
ER -
Sandeep Kumar, Daniel P. Palomar. (2018). PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. IEEE SigPort. http://sigport.org/3073
Sandeep Kumar, Daniel P. Palomar, 2018. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. Available at: http://sigport.org/3073.
Sandeep Kumar, Daniel P. Palomar. (2018). "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA." Web.
1. Sandeep Kumar, Daniel P. Palomar. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3073

PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA

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Authors:
Sandeep Kumar, Daniel P. Palomar
Submitted On:
20 April 2018 - 1:08am
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poster_ICASSP2018.pdf

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[1] Sandeep Kumar, Daniel P. Palomar, "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3070. Accessed: May. 23, 2018.
@article{3070-18,
url = {http://sigport.org/3070},
author = {Sandeep Kumar; Daniel P. Palomar },
publisher = {IEEE SigPort},
title = {PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA},
year = {2018} }
TY - EJOUR
T1 - PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA
AU - Sandeep Kumar; Daniel P. Palomar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3070
ER -
Sandeep Kumar, Daniel P. Palomar. (2018). PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. IEEE SigPort. http://sigport.org/3070
Sandeep Kumar, Daniel P. Palomar, 2018. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA. Available at: http://sigport.org/3070.
Sandeep Kumar, Daniel P. Palomar. (2018). "PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA." Web.
1. Sandeep Kumar, Daniel P. Palomar. PARAMETER ESTIMATION OF HEAVY-TAILED RANDOM WALK MODEL FROM INCOMPLETE DATA [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3070

Demixing and blind deconvolution of graph-diffused signals


We extend the classical joint problem of signal demixing, blind deconvolution,
and filter identification to the realm of graphs. The model is that
each mixing signal is generated by a sparse input diffused via a graph filter.
Then, the sum of diffused signals is observed. We identify and address
two problems: 1) each sparse input is diffused in a different graph; and 2)
all signals are diffused in the same graph. These tasks amount to finding
the collections of sources and filter coefficients producing the observation.

Paper Details

Authors:
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez
Submitted On:
19 April 2018 - 4:51pm
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ICASSP2018_demixing_GSP_poster_v2.pdf

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[1] Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez, "Demixing and blind deconvolution of graph-diffused signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3027. Accessed: May. 23, 2018.
@article{3027-18,
url = {http://sigport.org/3027},
author = {Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez },
publisher = {IEEE SigPort},
title = {Demixing and blind deconvolution of graph-diffused signals},
year = {2018} }
TY - EJOUR
T1 - Demixing and blind deconvolution of graph-diffused signals
AU - Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3027
ER -
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. (2018). Demixing and blind deconvolution of graph-diffused signals. IEEE SigPort. http://sigport.org/3027
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez, 2018. Demixing and blind deconvolution of graph-diffused signals. Available at: http://sigport.org/3027.
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. (2018). "Demixing and blind deconvolution of graph-diffused signals." Web.
1. Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. Demixing and blind deconvolution of graph-diffused signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3027

A Dimension-Independent Discriminant between Distributions


Henze-Penrose divergence is a non-parametric divergence measure that can be used to estimate a bound on the Bayes error in a binary classification problem. In this paper, we show that a cross- match statistic based on optimal weighted matching can be used to directly estimate Henze-Penrose divergence. Unlike an earlier approach based on the Friedman-Rafsky minimal spanning tree statistic, the proposed method is dimension-independent. The new approach is evaluated using simulation and applied to real datasets to obtain Bayes error estimates.

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Authors:
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero
Submitted On:
19 April 2018 - 12:32pm
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icassp2018.pdf

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[1] Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero, "A Dimension-Independent Discriminant between Distributions", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2982. Accessed: May. 23, 2018.
@article{2982-18,
url = {http://sigport.org/2982},
author = {Salimeh Yasaei-Sekeh; Brandon Oselio; Alfred Hero },
publisher = {IEEE SigPort},
title = {A Dimension-Independent Discriminant between Distributions},
year = {2018} }
TY - EJOUR
T1 - A Dimension-Independent Discriminant between Distributions
AU - Salimeh Yasaei-Sekeh; Brandon Oselio; Alfred Hero
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2982
ER -
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero. (2018). A Dimension-Independent Discriminant between Distributions. IEEE SigPort. http://sigport.org/2982
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero, 2018. A Dimension-Independent Discriminant between Distributions. Available at: http://sigport.org/2982.
Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero. (2018). "A Dimension-Independent Discriminant between Distributions." Web.
1. Salimeh Yasaei-Sekeh, Brandon Oselio, Alfred Hero. A Dimension-Independent Discriminant between Distributions [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2982

Matching Pursuit Based Convolutional Sparse Coding


Sparse coding techniques for image processing traditionally rely on processing small overlapping patches separately followed by averaging. This has the disadvantage that the reconstructed image no longer obeys the sparsity prior used in the processing. For this purpose convolutional sparse coding has been introduced, where a shift-invariant dictionary is used and the sparsity of the recovered image is maintained. Most such strategies target the $\ell_0$ ``norm'' of the whole image, which may create an imbalanced sparsity across various regions in the image.

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Authors:
Elad Plaut, Raja Giryes
Submitted On:
18 April 2018 - 11:44pm
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ICASSP2018_ConvolutionalSparseCoding.pptx

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[1] Elad Plaut, Raja Giryes, "Matching Pursuit Based Convolutional Sparse Coding", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2978. Accessed: May. 23, 2018.
@article{2978-18,
url = {http://sigport.org/2978},
author = {Elad Plaut; Raja Giryes },
publisher = {IEEE SigPort},
title = {Matching Pursuit Based Convolutional Sparse Coding},
year = {2018} }
TY - EJOUR
T1 - Matching Pursuit Based Convolutional Sparse Coding
AU - Elad Plaut; Raja Giryes
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2978
ER -
Elad Plaut, Raja Giryes. (2018). Matching Pursuit Based Convolutional Sparse Coding. IEEE SigPort. http://sigport.org/2978
Elad Plaut, Raja Giryes, 2018. Matching Pursuit Based Convolutional Sparse Coding. Available at: http://sigport.org/2978.
Elad Plaut, Raja Giryes. (2018). "Matching Pursuit Based Convolutional Sparse Coding." Web.
1. Elad Plaut, Raja Giryes. Matching Pursuit Based Convolutional Sparse Coding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2978

Poster ICASSP

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Authors:
Herve Carfantan, Sebastien Bourguignon
Submitted On:
15 April 2018 - 12:53am
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4527_Boudineau_Poster.pdf

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[1] Herve Carfantan, Sebastien Bourguignon, "Poster ICASSP", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2879. Accessed: May. 23, 2018.
@article{2879-18,
url = {http://sigport.org/2879},
author = {Herve Carfantan; Sebastien Bourguignon },
publisher = {IEEE SigPort},
title = {Poster ICASSP},
year = {2018} }
TY - EJOUR
T1 - Poster ICASSP
AU - Herve Carfantan; Sebastien Bourguignon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2879
ER -
Herve Carfantan, Sebastien Bourguignon. (2018). Poster ICASSP. IEEE SigPort. http://sigport.org/2879
Herve Carfantan, Sebastien Bourguignon, 2018. Poster ICASSP. Available at: http://sigport.org/2879.
Herve Carfantan, Sebastien Bourguignon. (2018). "Poster ICASSP." Web.
1. Herve Carfantan, Sebastien Bourguignon. Poster ICASSP [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2879

TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION


The problem of identification of a nonstationary autoregressive process
with unknown, and possibly time-varying, rate of parameter
changes, is considered and solved using the parallel estimation approach.
The proposed two-stage estimation scheme, which combines
the local estimation approach with the basis function one, offers
both quantitative and qualitative improvements compared with
the currently used single-stage methods.

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Authors:
Maciej Niedzwiecki, Marcin Ciolek
Submitted On:
13 April 2018 - 3:09pm
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ICASSP2018 v8.pdf

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[1] Maciej Niedzwiecki, Marcin Ciolek, "TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2750. Accessed: May. 23, 2018.
@article{2750-18,
url = {http://sigport.org/2750},
author = {Maciej Niedzwiecki; Marcin Ciolek },
publisher = {IEEE SigPort},
title = {TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION},
year = {2018} }
TY - EJOUR
T1 - TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION
AU - Maciej Niedzwiecki; Marcin Ciolek
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2750
ER -
Maciej Niedzwiecki, Marcin Ciolek. (2018). TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION. IEEE SigPort. http://sigport.org/2750
Maciej Niedzwiecki, Marcin Ciolek, 2018. TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION. Available at: http://sigport.org/2750.
Maciej Niedzwiecki, Marcin Ciolek. (2018). "TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION." Web.
1. Maciej Niedzwiecki, Marcin Ciolek. TWO-STAGE IDENTIFICATION OF LOCALLY STATIONARY AUTOREGRESSIVE PROCESSES AND ITS APPLICATION TO THE PARAMETRIC SPECTRUM ESTIMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2750

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