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Machine Learning for Signal Processing

DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES

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Authors:
Or Yair, Danny Eytan, Ronen Talmon
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10 May 2019 - 2:37pm
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[1] Or Yair, Danny Eytan, Ronen Talmon, " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4389. Accessed: May. 23, 2019.
@article{4389-19,
url = {http://sigport.org/4389},
author = {Or Yair; Danny Eytan; Ronen Talmon },
publisher = {IEEE SigPort},
title = { DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES},
year = {2019} }
TY - EJOUR
T1 - DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES
AU - Or Yair; Danny Eytan; Ronen Talmon
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4389
ER -
Or Yair, Danny Eytan, Ronen Talmon. (2019). DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. IEEE SigPort. http://sigport.org/4389
Or Yair, Danny Eytan, Ronen Talmon, 2019. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. Available at: http://sigport.org/4389.
Or Yair, Danny Eytan, Ronen Talmon. (2019). " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES." Web.
1. Or Yair, Danny Eytan, Ronen Talmon. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4389

DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES

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Authors:
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon
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10 May 2019 - 2:37pm
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[1] Gal Maman, Or Yair, Danny Eytan, Ronen Talmon, " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4387. Accessed: May. 23, 2019.
@article{4387-19,
url = {http://sigport.org/4387},
author = {Gal Maman; Or Yair; Danny Eytan; Ronen Talmon },
publisher = {IEEE SigPort},
title = { DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES},
year = {2019} }
TY - EJOUR
T1 - DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES
AU - Gal Maman; Or Yair; Danny Eytan; Ronen Talmon
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4387
ER -
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon. (2019). DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. IEEE SigPort. http://sigport.org/4387
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon, 2019. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES. Available at: http://sigport.org/4387.
Gal Maman, Or Yair, Danny Eytan, Ronen Talmon. (2019). " DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES." Web.
1. Gal Maman, Or Yair, Danny Eytan, Ronen Talmon. DOMAIN ADAPTATION USING RIEMANNIAN GEOMETRY OF SPD MATRICES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4387

Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning

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10 May 2019 - 1:06pm
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[1] , "Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4374. Accessed: May. 23, 2019.
@article{4374-19,
url = {http://sigport.org/4374},
author = { },
publisher = {IEEE SigPort},
title = {Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning},
year = {2019} }
TY - EJOUR
T1 - Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4374
ER -
. (2019). Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning. IEEE SigPort. http://sigport.org/4374
, 2019. Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning. Available at: http://sigport.org/4374.
. (2019). "Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning." Web.
1. . Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4374

Prediction of multi-target dynamics using discrete descriptors: An interactive approach


We propose a probabilistic method to track and interpret interactions of moving objects. The proposed method is based on the analysis of location data from different moving objects that modify their dynamics according to rules of interactions, namely attractive and repulsive forces governing moving objects in a scene. Our method uses a Bayesian structure to identify key elements of the interplay rules and facilitates the prediction of objects' dynamics as the interacting system.

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Authors:
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni
Submitted On:
10 May 2019 - 12:18pm
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[1] M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni , "Prediction of multi-target dynamics using discrete descriptors: An interactive approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4369. Accessed: May. 23, 2019.
@article{4369-19,
url = {http://sigport.org/4369},
author = {M. Baydoun; D. Campo; D. Kanapram; L. Marcenaro; C. Regazzoni },
publisher = {IEEE SigPort},
title = {Prediction of multi-target dynamics using discrete descriptors: An interactive approach},
year = {2019} }
TY - EJOUR
T1 - Prediction of multi-target dynamics using discrete descriptors: An interactive approach
AU - M. Baydoun; D. Campo; D. Kanapram; L. Marcenaro; C. Regazzoni
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4369
ER -
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni . (2019). Prediction of multi-target dynamics using discrete descriptors: An interactive approach. IEEE SigPort. http://sigport.org/4369
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni , 2019. Prediction of multi-target dynamics using discrete descriptors: An interactive approach. Available at: http://sigport.org/4369.
M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni . (2019). "Prediction of multi-target dynamics using discrete descriptors: An interactive approach." Web.
1. M. Baydoun, D. Campo, D. Kanapram, L. Marcenaro, C. Regazzoni . Prediction of multi-target dynamics using discrete descriptors: An interactive approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4369

COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING

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Authors:
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos
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10 May 2019 - 10:54am
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[1] Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos, "COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4352. Accessed: May. 23, 2019.
@article{4352-19,
url = {http://sigport.org/4352},
author = {Konstantinos Makantasis; ; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos },
publisher = {IEEE SigPort},
title = {COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING},
year = {2019} }
TY - EJOUR
T1 - COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING
AU - Konstantinos Makantasis; ; Anastasios Doulamis; Nikolaos Doulamis; Athanasios Voulodimos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4352
ER -
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. (2019). COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING. IEEE SigPort. http://sigport.org/4352
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos, 2019. COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING. Available at: http://sigport.org/4352.
Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. (2019). "COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING." Web.
1. Konstantinos Makantasis, , Anastasios Doulamis, Nikolaos Doulamis, Athanasios Voulodimos. COMMON MODE PATTERNS FOR SUPERVISED TENSOR SUBSPACE LEARNING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4352

JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA


CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging. This work proposes a new deep convolutional neural network (CNN), called JSR-Net, that jointly reconstructs CT images and their associated Radon domain projections. JSR-Net combines the traditional model-based approach with deep architecture design of deep learning. A hybrid loss function is adapted to improve the performance of the JSR-Net making it more effective in protecting important image structures.

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Authors:
Haimiao Zhang, Bin Dong, Baodong Liu
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10 May 2019 - 3:25am
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[1] Haimiao Zhang, Bin Dong, Baodong Liu, "JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4281. Accessed: May. 23, 2019.
@article{4281-19,
url = {http://sigport.org/4281},
author = {Haimiao Zhang; Bin Dong; Baodong Liu },
publisher = {IEEE SigPort},
title = {JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA},
year = {2019} }
TY - EJOUR
T1 - JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA
AU - Haimiao Zhang; Bin Dong; Baodong Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4281
ER -
Haimiao Zhang, Bin Dong, Baodong Liu. (2019). JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA. IEEE SigPort. http://sigport.org/4281
Haimiao Zhang, Bin Dong, Baodong Liu, 2019. JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA. Available at: http://sigport.org/4281.
Haimiao Zhang, Bin Dong, Baodong Liu. (2019). "JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA." Web.
1. Haimiao Zhang, Bin Dong, Baodong Liu. JSR-NET: A DEEP NETWORK FOR JOINT SPATIAL-RADON DOMAIN CT RECON- STRUCTION FROM INCOMPLETE DATA [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4281

Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis


It is well known that electromagnetic and power side-channel attacks allow extraction of unintended information from a computer processor. However, little work has been done to quantify how small a sample is needed in order to glean meaningful information about a program's execution. This paper quantifies this minimum context by training a deep-learning model to track and classify program block types given small windows of side-channel data. We show that a window containing approximately four clock cycles suffices to predict block type with our experimental setup.

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Authors:
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin
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9 May 2019 - 8:19am
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Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis

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[1] Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin, "Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4196. Accessed: May. 23, 2019.
@article{4196-19,
url = {http://sigport.org/4196},
author = {Louis Jensen; Gavin Brown; Xiao Wang; Jacob Harer; Sang Chin },
publisher = {IEEE SigPort},
title = {Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis},
year = {2019} }
TY - EJOUR
T1 - Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis
AU - Louis Jensen; Gavin Brown; Xiao Wang; Jacob Harer; Sang Chin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4196
ER -
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin. (2019). Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis. IEEE SigPort. http://sigport.org/4196
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin, 2019. Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis. Available at: http://sigport.org/4196.
Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin. (2019). "Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis." Web.
1. Louis Jensen, Gavin Brown, Xiao Wang, Jacob Harer, Sang Chin. Deep learning for Minimal Context Classification of Block-types through Side-Channel Analysis [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4196

A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface


One of the major limitations of current electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is the long calibration time. Due to a high level of noise and non-stationarity inherent in EEG signals, a calibration model trained using the limited number of train data may not yield an accurate BCI model. To address this problem, this paper proposes a novel subject-to-subject transfer learning framework that improves the classification accuracy using limited training data.

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Authors:
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh
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9 May 2019 - 7:04am
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[1] Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh, "A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4188. Accessed: May. 23, 2019.
@article{4188-19,
url = {http://sigport.org/4188},
author = {Joshua Giles; Kai Keng Ang; Lyudmila S. Mihaylova; Mahnaz Arvaneh },
publisher = {IEEE SigPort},
title = {A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface},
year = {2019} }
TY - EJOUR
T1 - A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface
AU - Joshua Giles; Kai Keng Ang; Lyudmila S. Mihaylova; Mahnaz Arvaneh
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4188
ER -
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. (2019). A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface. IEEE SigPort. http://sigport.org/4188
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh, 2019. A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface. Available at: http://sigport.org/4188.
Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. (2019). "A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface." Web.
1. Joshua Giles, Kai Keng Ang, Lyudmila S. Mihaylova, Mahnaz Arvaneh. A Subject-to-Subject Transfer Learning Framework based on Jensen-Shannon divergence for Improving Brain-computer Interface [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4188

NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION


Obtaining aligned spectral pairs in case of non-parallel data for stand-alone Voice Conversion (VC) technique is a challenging research problem. Unsupervised alignment algorithm, namely, an Iterative combination of a Nearest Neighbor search step and a Conversion step Alignment (INCA) iteratively tries to align the spectral features by minimizing the Euclidean distance metric between the intermediate converted and the target spectral feature vectors.

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Authors:
Nirmesh Shah, Hemant A. Patil
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8 May 2019 - 8:07am
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[1] Nirmesh Shah, Hemant A. Patil, "NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4080. Accessed: May. 23, 2019.
@article{4080-19,
url = {http://sigport.org/4080},
author = {Nirmesh Shah; Hemant A. Patil },
publisher = {IEEE SigPort},
title = {NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION},
year = {2019} }
TY - EJOUR
T1 - NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION
AU - Nirmesh Shah; Hemant A. Patil
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4080
ER -
Nirmesh Shah, Hemant A. Patil. (2019). NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION. IEEE SigPort. http://sigport.org/4080
Nirmesh Shah, Hemant A. Patil, 2019. NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION. Available at: http://sigport.org/4080.
Nirmesh Shah, Hemant A. Patil. (2019). "NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION." Web.
1. Nirmesh Shah, Hemant A. Patil. NOVEL METRIC LEARNING FOR NON-PARALLEL VOICE CONVERSION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4080

Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition


Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share
some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent
years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multi-
block tensor data, we propose a fast double-coupled nonnegative Canonical Polyadic Decomposition (FDC-NCPD)

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Authors:
Tapani Ristaniemi, Fengyu Cong
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8 May 2019 - 6:25am
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[1] Tapani Ristaniemi, Fengyu Cong, "Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4067. Accessed: May. 23, 2019.
@article{4067-19,
url = {http://sigport.org/4067},
author = {Tapani Ristaniemi; Fengyu Cong },
publisher = {IEEE SigPort},
title = {Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition},
year = {2019} }
TY - EJOUR
T1 - Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition
AU - Tapani Ristaniemi; Fengyu Cong
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4067
ER -
Tapani Ristaniemi, Fengyu Cong. (2019). Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition. IEEE SigPort. http://sigport.org/4067
Tapani Ristaniemi, Fengyu Cong, 2019. Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition. Available at: http://sigport.org/4067.
Tapani Ristaniemi, Fengyu Cong. (2019). "Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition." Web.
1. Tapani Ristaniemi, Fengyu Cong. Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4067

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