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Communications and Networking

High-speed Optical Camera Communication Using an Optimally Modulated Signal


This paper describes a high-speed optical camera communication (OCC) technique using an LED and a rolling-shutter camera. In the proposed technique, the symbols being transmitted are encoded as time delays of optimally modulated signals derived theoretically. A receiver decodes the symbols by using intensities obtained from four consecutive line sensors of a camera.

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Authors:
Hayato Kumaki, Takayuki Akiyama, Hiromichi Hashizume
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20 April 2018 - 5:32am
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skah-icassp2018-poster.pdf

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[1] Hayato Kumaki, Takayuki Akiyama, Hiromichi Hashizume, "High-speed Optical Camera Communication Using an Optimally Modulated Signal", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3099. Accessed: May. 24, 2018.
@article{3099-18,
url = {http://sigport.org/3099},
author = {Hayato Kumaki; Takayuki Akiyama; Hiromichi Hashizume },
publisher = {IEEE SigPort},
title = {High-speed Optical Camera Communication Using an Optimally Modulated Signal},
year = {2018} }
TY - EJOUR
T1 - High-speed Optical Camera Communication Using an Optimally Modulated Signal
AU - Hayato Kumaki; Takayuki Akiyama; Hiromichi Hashizume
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3099
ER -
Hayato Kumaki, Takayuki Akiyama, Hiromichi Hashizume. (2018). High-speed Optical Camera Communication Using an Optimally Modulated Signal. IEEE SigPort. http://sigport.org/3099
Hayato Kumaki, Takayuki Akiyama, Hiromichi Hashizume, 2018. High-speed Optical Camera Communication Using an Optimally Modulated Signal. Available at: http://sigport.org/3099.
Hayato Kumaki, Takayuki Akiyama, Hiromichi Hashizume. (2018). "High-speed Optical Camera Communication Using an Optimally Modulated Signal." Web.
1. Hayato Kumaki, Takayuki Akiyama, Hiromichi Hashizume. High-speed Optical Camera Communication Using an Optimally Modulated Signal [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3099

DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS


In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution.

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Authors:
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson
Submitted On:
19 April 2018 - 5:17pm
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Deep Learning for FEP Prediction.pdf

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[1] Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson, "DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3030. Accessed: May. 24, 2018.
@article{3030-18,
url = {http://sigport.org/3030},
author = {Vidit Saxena; Joakim Jaldén; Hugo Tullberg; Mats Bengtsson },
publisher = {IEEE SigPort},
title = {DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS},
year = {2018} }
TY - EJOUR
T1 - DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS
AU - Vidit Saxena; Joakim Jaldén; Hugo Tullberg; Mats Bengtsson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3030
ER -
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. (2018). DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS. IEEE SigPort. http://sigport.org/3030
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson, 2018. DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS. Available at: http://sigport.org/3030.
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. (2018). "DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS." Web.
1. Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3030

A compressive sensing-based active user and symbol detection for massive machine type communications

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18 April 2018 - 1:10am
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icassp18_r3.pdf

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[1] , "A compressive sensing-based active user and symbol detection for massive machine type communications", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2959. Accessed: May. 24, 2018.
@article{2959-18,
url = {http://sigport.org/2959},
author = { },
publisher = {IEEE SigPort},
title = {A compressive sensing-based active user and symbol detection for massive machine type communications},
year = {2018} }
TY - EJOUR
T1 - A compressive sensing-based active user and symbol detection for massive machine type communications
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2959
ER -
. (2018). A compressive sensing-based active user and symbol detection for massive machine type communications. IEEE SigPort. http://sigport.org/2959
, 2018. A compressive sensing-based active user and symbol detection for massive machine type communications. Available at: http://sigport.org/2959.
. (2018). "A compressive sensing-based active user and symbol detection for massive machine type communications." Web.
1. . A compressive sensing-based active user and symbol detection for massive machine type communications [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2959

Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding


Machine-type communication requires rethinking of the structure of short packets due to the coding limitations and the significant role of the control information. In ultra-reliable low-latency communication (URLLC), it is crucial to optimally use the limited degrees of freedom (DoFs) to send data and control information. We consider a URLLC model for short packet transmission with acknowledgement (ACK).

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Authors:
Alexandru-Sabin Bana, Kasper Fløe Trillingsgaard, Petar Popovski, Elisabeth de Carvalho
Submitted On:
14 April 2018 - 5:56pm
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icassp_ASB_short_pkt_struc_v3.pdf

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[1] Alexandru-Sabin Bana, Kasper Fløe Trillingsgaard, Petar Popovski, Elisabeth de Carvalho, "Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2856. Accessed: May. 24, 2018.
@article{2856-18,
url = {http://sigport.org/2856},
author = {Alexandru-Sabin Bana; Kasper Fløe Trillingsgaard; Petar Popovski; Elisabeth de Carvalho },
publisher = {IEEE SigPort},
title = {Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding},
year = {2018} }
TY - EJOUR
T1 - Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding
AU - Alexandru-Sabin Bana; Kasper Fløe Trillingsgaard; Petar Popovski; Elisabeth de Carvalho
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2856
ER -
Alexandru-Sabin Bana, Kasper Fløe Trillingsgaard, Petar Popovski, Elisabeth de Carvalho. (2018). Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding. IEEE SigPort. http://sigport.org/2856
Alexandru-Sabin Bana, Kasper Fløe Trillingsgaard, Petar Popovski, Elisabeth de Carvalho, 2018. Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding. Available at: http://sigport.org/2856.
Alexandru-Sabin Bana, Kasper Fløe Trillingsgaard, Petar Popovski, Elisabeth de Carvalho. (2018). "Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding." Web.
1. Alexandru-Sabin Bana, Kasper Fløe Trillingsgaard, Petar Popovski, Elisabeth de Carvalho. Short packet structure for ultra-reliable machine-type communication: tradeoff between detection and decoding [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2856

IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL

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Authors:
Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone
Submitted On:
14 April 2018 - 10:41am
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poster.pdf

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[1] Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone, "IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2836. Accessed: May. 24, 2018.
@article{2836-18,
url = {http://sigport.org/2836},
author = {Nadhir Ben Rached; Zdravko Botev; Abla Kammoun; Mohamed-Slim Alouini; Raul Tempone },
publisher = {IEEE SigPort},
title = {IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL},
year = {2018} }
TY - EJOUR
T1 - IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL
AU - Nadhir Ben Rached; Zdravko Botev; Abla Kammoun; Mohamed-Slim Alouini; Raul Tempone
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2836
ER -
Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone. (2018). IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL. IEEE SigPort. http://sigport.org/2836
Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone, 2018. IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL. Available at: http://sigport.org/2836.
Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone. (2018). "IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL." Web.
1. Nadhir Ben Rached, Zdravko Botev, Abla Kammoun, Mohamed-Slim Alouini, Raul Tempone. IMPORTANCE SAMPLING ESTIMATOR OF OUTAGE PROBABILITY UNDER GENERALIZED SELECTION COMBINING MODEL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2836

FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL


We consider the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to the majority of existing NOMA schemes using continuous Gaussian distributed inputs, we consider practical quadrature amplitude modulation (QAM) constel- lations at both transmitters, whose sizes are not necessarily the same.

poster.pdf

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Authors:
Zheng Dong, He (Henry) Chen, Jian-Kang Zhang, Lei Huang, and Branka Vucetic
Submitted On:
12 April 2018 - 8:55pm
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poster.pdf

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[1] Zheng Dong, He (Henry) Chen, Jian-Kang Zhang, Lei Huang, and Branka Vucetic, "FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2530. Accessed: May. 24, 2018.
@article{2530-18,
url = {http://sigport.org/2530},
author = {Zheng Dong; He (Henry) Chen; Jian-Kang Zhang; Lei Huang; and Branka Vucetic },
publisher = {IEEE SigPort},
title = {FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL},
year = {2018} }
TY - EJOUR
T1 - FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL
AU - Zheng Dong; He (Henry) Chen; Jian-Kang Zhang; Lei Huang; and Branka Vucetic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2530
ER -
Zheng Dong, He (Henry) Chen, Jian-Kang Zhang, Lei Huang, and Branka Vucetic. (2018). FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL. IEEE SigPort. http://sigport.org/2530
Zheng Dong, He (Henry) Chen, Jian-Kang Zhang, Lei Huang, and Branka Vucetic, 2018. FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL. Available at: http://sigport.org/2530.
Zheng Dong, He (Henry) Chen, Jian-Kang Zhang, Lei Huang, and Branka Vucetic. (2018). "FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL." Web.
1. Zheng Dong, He (Henry) Chen, Jian-Kang Zhang, Lei Huang, and Branka Vucetic. FINITE-ALPHABET NOMA FOR TWO-USER UPLINK CHANNEL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2530

UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY

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12 April 2018 - 11:29am
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Underwater Optical Sensor Networks Localization

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[1] , "UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2400. Accessed: May. 24, 2018.
@article{2400-18,
url = {http://sigport.org/2400},
author = { },
publisher = {IEEE SigPort},
title = {UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY},
year = {2018} }
TY - EJOUR
T1 - UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2400
ER -
. (2018). UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY. IEEE SigPort. http://sigport.org/2400
, 2018. UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY. Available at: http://sigport.org/2400.
. (2018). "UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY." Web.
1. . UNDERWATER OPTICAL SENSOR NETWORKS LOCALIZATION WITH LIMITED CONNECTIVITY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2400

A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization


Data ecosystems are becoming larger and more complex, while privacy concerns are threatening to erode their potential benefits. Recently, users have developed obfuscation techniques that issue fake search engine queries, undermine location tracking algorithms, or evade government surveillance. These techniques raise one conflict between each user and the machine learning algorithms which track the users, and one conflict between the users themselves. We use game theory to capture the first conflict with a Stackelberg game and the second conflict with a mean field game.

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Authors:
Jeffrey Pawlick, Quanyan Zhu
Submitted On:
9 November 2017 - 2:35pm
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GlobalSipPawlickZhu.pdf

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[1] Jeffrey Pawlick, Quanyan Zhu, "A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2275. Accessed: May. 24, 2018.
@article{2275-17,
url = {http://sigport.org/2275},
author = {Jeffrey Pawlick; Quanyan Zhu },
publisher = {IEEE SigPort},
title = {A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization},
year = {2017} }
TY - EJOUR
T1 - A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization
AU - Jeffrey Pawlick; Quanyan Zhu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2275
ER -
Jeffrey Pawlick, Quanyan Zhu. (2017). A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization. IEEE SigPort. http://sigport.org/2275
Jeffrey Pawlick, Quanyan Zhu, 2017. A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization. Available at: http://sigport.org/2275.
Jeffrey Pawlick, Quanyan Zhu. (2017). "A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization." Web.
1. Jeffrey Pawlick, Quanyan Zhu. A Mean-Field Stackelberg Game Approach for Obfuscation Adoption in Empirical Risk Minimization [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2275

Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection

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Authors:
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang
Submitted On:
16 September 2017 - 8:32am
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Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection.pdf

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[1] Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang, "Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2191. Accessed: May. 24, 2018.
@article{2191-17,
url = {http://sigport.org/2191},
author = {Kennedy W. Wangila; Pengfei Zhu; Qinghua Hu; Changqing Zhang },
publisher = {IEEE SigPort},
title = {Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection},
year = {2017} }
TY - EJOUR
T1 - Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection
AU - Kennedy W. Wangila; Pengfei Zhu; Qinghua Hu; Changqing Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2191
ER -
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang. (2017). Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection. IEEE SigPort. http://sigport.org/2191
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang, 2017. Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection. Available at: http://sigport.org/2191.
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang. (2017). "Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection." Web.
1. Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang. Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2191

Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection

Paper Details

Authors:
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang
Submitted On:
16 September 2017 - 8:32am
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Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection.pdf

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[1] Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang, "Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2190. Accessed: May. 24, 2018.
@article{2190-17,
url = {http://sigport.org/2190},
author = {Kennedy W. Wangila; Pengfei Zhu; Qinghua Hu; Changqing Zhang },
publisher = {IEEE SigPort},
title = {Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection},
year = {2017} }
TY - EJOUR
T1 - Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection
AU - Kennedy W. Wangila; Pengfei Zhu; Qinghua Hu; Changqing Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2190
ER -
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang. (2017). Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection. IEEE SigPort. http://sigport.org/2190
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang, 2017. Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection. Available at: http://sigport.org/2190.
Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang. (2017). "Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection." Web.
1. Kennedy W. Wangila, Pengfei Zhu, Qinghua Hu, Changqing Zhang. Mixed Sparsity Regularized Multi-view Unsupervised Feature Selection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2190

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