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Machine Learning for Signal Processing

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: Dec. 13, 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
Submitted On:
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: Dec. 13, 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
Submitted On:
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: Dec. 13, 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
Submitted On:
9 May 2019 - 7:04am
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ICASSP POSTER.pdf

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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: Dec. 13, 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
Submitted On:
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: Dec. 13, 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
Submitted On:
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: Dec. 13, 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

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)

Paper Details

Authors:
Tapani Ristaniemi, Fengyu Cong
Submitted On:
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/4063. Accessed: Dec. 13, 2019.
@article{4063-19,
url = {http://sigport.org/4063},
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/4063
ER -
Tapani Ristaniemi, Fengyu Cong. (2019). Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition. IEEE SigPort. http://sigport.org/4063
Tapani Ristaniemi, Fengyu Cong, 2019. Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition. Available at: http://sigport.org/4063.
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/4063

Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors


This paper proposes a novel framework to regularize the highly illposed and non-linear Fourier ptychography problem using generative models. We demonstrate experimentally that our proposed algorithm, Deep Ptych, outperforms the existing Fourier ptychography techniques, in terms of quality of reconstruction and robustness against noise, using far fewer samples. We further modify the proposed approach to allow the generative model to explore solutions outside the range, leading to improved performance.

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Authors:
Fahad Shamshad, Farwa Abbas, Ali Ahmed
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8 May 2019 - 3:48am
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[1] Fahad Shamshad, Farwa Abbas, Ali Ahmed, "Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4038. Accessed: Dec. 13, 2019.
@article{4038-19,
url = {http://sigport.org/4038},
author = {Fahad Shamshad; Farwa Abbas; Ali Ahmed },
publisher = {IEEE SigPort},
title = {Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors},
year = {2019} }
TY - EJOUR
T1 - Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors
AU - Fahad Shamshad; Farwa Abbas; Ali Ahmed
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4038
ER -
Fahad Shamshad, Farwa Abbas, Ali Ahmed. (2019). Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors. IEEE SigPort. http://sigport.org/4038
Fahad Shamshad, Farwa Abbas, Ali Ahmed, 2019. Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors. Available at: http://sigport.org/4038.
Fahad Shamshad, Farwa Abbas, Ali Ahmed. (2019). "Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors." Web.
1. Fahad Shamshad, Farwa Abbas, Ali Ahmed. Deep Ptych: Subsampled Fourier Ptychography Using Deep Generative Priors [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4038

Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation

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Authors:
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li
Submitted On:
7 May 2019 - 9:56pm
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[1] Wei Chang, Feiping Nie, Rong Wang, Xuelong Li, "Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3987. Accessed: Dec. 13, 2019.
@article{3987-19,
url = {http://sigport.org/3987},
author = {Wei Chang; Feiping Nie; Rong Wang; Xuelong Li },
publisher = {IEEE SigPort},
title = {Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation},
year = {2019} }
TY - EJOUR
T1 - Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation
AU - Wei Chang; Feiping Nie; Rong Wang; Xuelong Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3987
ER -
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li. (2019). Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation. IEEE SigPort. http://sigport.org/3987
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li, 2019. Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation. Available at: http://sigport.org/3987.
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li. (2019). "Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation." Web.
1. Wei Chang, Feiping Nie, Rong Wang, Xuelong Li. Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3987

Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation

Paper Details

Authors:
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li
Submitted On:
7 May 2019 - 9:56pm
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[1] Wei Chang, Feiping Nie, Rong Wang, Xuelong Li, "Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3984. Accessed: Dec. 13, 2019.
@article{3984-19,
url = {http://sigport.org/3984},
author = {Wei Chang; Feiping Nie; Rong Wang; Xuelong Li },
publisher = {IEEE SigPort},
title = {Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation},
year = {2019} }
TY - EJOUR
T1 - Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation
AU - Wei Chang; Feiping Nie; Rong Wang; Xuelong Li
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3984
ER -
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li. (2019). Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation. IEEE SigPort. http://sigport.org/3984
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li, 2019. Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation. Available at: http://sigport.org/3984.
Wei Chang, Feiping Nie, Rong Wang, Xuelong Li. (2019). "Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation." Web.
1. Wei Chang, Feiping Nie, Rong Wang, Xuelong Li. Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3984

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