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Learning theory and algorithms (MLR-LEAR)

GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)


We reveal an interesting link between tensors and multivariate statistics. The rank of a multivariate probability tensor can be interpreted as a nonlinear measure of statistical dependence of the associated random variables. Rank equals one when the random variables are independent, and complete statistical dependence corresponds to full rank; but we show that rank as low as two can already model strong statistical dependence.

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Authors:
N.D. Sidiropoulos, N. Kargas, X. Fu
Submitted On:
24 December 2018 - 8:25pm
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GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)

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[1] N.D. Sidiropoulos, N. Kargas, X. Fu, "GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3842. Accessed: Mar. 21, 2019.
@article{3842-18,
url = {http://sigport.org/3842},
author = {N.D. Sidiropoulos; N. Kargas; X. Fu },
publisher = {IEEE SigPort},
title = {GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)},
year = {2018} }
TY - EJOUR
T1 - GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)
AU - N.D. Sidiropoulos; N. Kargas; X. Fu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3842
ER -
N.D. Sidiropoulos, N. Kargas, X. Fu. (2018). GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu). IEEE SigPort. http://sigport.org/3842
N.D. Sidiropoulos, N. Kargas, X. Fu, 2018. GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu). Available at: http://sigport.org/3842.
N.D. Sidiropoulos, N. Kargas, X. Fu. (2018). "GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu)." Web.
1. N.D. Sidiropoulos, N. Kargas, X. Fu. GlobalSIP 2018 Keynote: Tensors and Probability: An Intriguing Union (N. Sidiropoulos, N. Kargas, X. Fu) [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3842

Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization

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Authors:
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini
Submitted On:
26 November 2018 - 3:18pm
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globalsip18_ZOPSGD

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[1] Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini, "Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3784. Accessed: Mar. 21, 2019.
@article{3784-18,
url = {http://sigport.org/3784},
author = {Xingguo Li; Pin-Yu Chen; Jarvis Haupt; Lisa Amini },
publisher = {IEEE SigPort},
title = {Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization},
year = {2018} }
TY - EJOUR
T1 - Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization
AU - Xingguo Li; Pin-Yu Chen; Jarvis Haupt; Lisa Amini
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3784
ER -
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini. (2018). Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization. IEEE SigPort. http://sigport.org/3784
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini, 2018. Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization. Available at: http://sigport.org/3784.
Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini. (2018). "Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization." Web.
1. Xingguo Li, Pin-Yu Chen, Jarvis Haupt, Lisa Amini. Zeroth-Order Stochastic Projected Gradient Descent for Nonconvex Optimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3784

A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION


Deep neural networks (DNNs) have found applications in diverse signal processing (SP) problems. Most efforts either directly adopt the DNN as a black-box approach to perform certain SP tasks without taking into account of any known properties of the signal models, or insert a pre-defined SP operator into a DNN as an add-on data processing stage. This paper presents a novel hybrid-NN framework in which one or more SP layers are inserted into the DNN architecture in a coherent manner to enhance the network capability and efficiency in feature extraction.

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Authors:
Zhe Zhang, Xiang Chen, Zhi Tian
Submitted On:
26 November 2018 - 3:14pm
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Hybrid_NN_Poster_Zhe_new_2.pdf

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[1] Zhe Zhang, Xiang Chen, Zhi Tian, "A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3783. Accessed: Mar. 21, 2019.
@article{3783-18,
url = {http://sigport.org/3783},
author = {Zhe Zhang; Xiang Chen; Zhi Tian },
publisher = {IEEE SigPort},
title = {A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION},
year = {2018} }
TY - EJOUR
T1 - A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION
AU - Zhe Zhang; Xiang Chen; Zhi Tian
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3783
ER -
Zhe Zhang, Xiang Chen, Zhi Tian. (2018). A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION. IEEE SigPort. http://sigport.org/3783
Zhe Zhang, Xiang Chen, Zhi Tian, 2018. A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION. Available at: http://sigport.org/3783.
Zhe Zhang, Xiang Chen, Zhi Tian. (2018). "A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION." Web.
1. Zhe Zhang, Xiang Chen, Zhi Tian. A HYBRID NEURAL NETWORK FRAMEWORK AND APPLICATION TO RADAR AUTOMATIC TARGET RECOGNITION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3783

Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR


Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks. Motivated by adversarial machine learning, in this paper we investigate the robustness of sparse regression models with strongly correlated covariates to adversarially designed measurement noises. Specifically, we consider the family of ordered weighted L1 (OWL) regularized regression methods and study the case of OSCAR (octagonal shrinkage clustering algorithm for regression) in the adversarial setting.

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Authors:
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu
Submitted On:
23 November 2018 - 1:03pm
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globalsip(3).pdf

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[1] Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu, "Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3749. Accessed: Mar. 21, 2019.
@article{3749-18,
url = {http://sigport.org/3749},
author = {Pin-Yu Chen; Bhanukiran Vinzamuri and Sijia Liu },
publisher = {IEEE SigPort},
title = {Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR},
year = {2018} }
TY - EJOUR
T1 - Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR
AU - Pin-Yu Chen; Bhanukiran Vinzamuri and Sijia Liu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3749
ER -
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu. (2018). Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR. IEEE SigPort. http://sigport.org/3749
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu, 2018. Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR. Available at: http://sigport.org/3749.
Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu. (2018). "Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR." Web.
1. Pin-Yu Chen, Bhanukiran Vinzamuri and Sijia Liu. Is Ordered Weighed L1 Regularized Regression Robust to Adversarial Perturbation ? A Case Study on OSCAR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3749

IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX


This paper presents a simple yet effective method to improve the visual quality of Generative Adversarial Network (GAN) generated images. In typical GAN architectures, the discriminator block is designed mainly to capture the class-specific content from images without explicitly imposing constraints on the visual quality of the generated images. A key insight from the image quality assessment literature is that natural scenes possess a very unique local structural and (hence) statistical signature, and that distortions affect this signature.

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Submitted On:
7 October 2018 - 6:01am
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ICIP_2018_BEGAN.pdf

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[1] , "IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3590. Accessed: Mar. 21, 2019.
@article{3590-18,
url = {http://sigport.org/3590},
author = { },
publisher = {IEEE SigPort},
title = {IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX},
year = {2018} }
TY - EJOUR
T1 - IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3590
ER -
. (2018). IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX. IEEE SigPort. http://sigport.org/3590
, 2018. IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX. Available at: http://sigport.org/3590.
. (2018). "IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX." Web.
1. . IMPROVING THE VISUAL QUALITY OF GENERATIVE ADVERSARIAL NETWORK (GAN)-GENERATED IMAGES USING THE MULTI-SCALE STRUCTURAL SIMILARITY INDEX [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3590

Learning Flexible Representations of Stochastic Processes on Graphs


Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are developed for scalar functions supported on undirected graphs. We propose both a generalization of the underlying graph and a class of linear operations for stochastic (time-varying) processes on directed (or undirected) graphs to be used in graph convolutional networks.

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Authors:
Brian M. Sadler, Radu V. Balan
Submitted On:
30 May 2018 - 1:35pm
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[1] Brian M. Sadler, Radu V. Balan, "Learning Flexible Representations of Stochastic Processes on Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3220. Accessed: Mar. 21, 2019.
@article{3220-18,
url = {http://sigport.org/3220},
author = {Brian M. Sadler; Radu V. Balan },
publisher = {IEEE SigPort},
title = {Learning Flexible Representations of Stochastic Processes on Graphs},
year = {2018} }
TY - EJOUR
T1 - Learning Flexible Representations of Stochastic Processes on Graphs
AU - Brian M. Sadler; Radu V. Balan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3220
ER -
Brian M. Sadler, Radu V. Balan. (2018). Learning Flexible Representations of Stochastic Processes on Graphs. IEEE SigPort. http://sigport.org/3220
Brian M. Sadler, Radu V. Balan, 2018. Learning Flexible Representations of Stochastic Processes on Graphs. Available at: http://sigport.org/3220.
Brian M. Sadler, Radu V. Balan. (2018). "Learning Flexible Representations of Stochastic Processes on Graphs." Web.
1. Brian M. Sadler, Radu V. Balan. Learning Flexible Representations of Stochastic Processes on Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3220

Semi-Blind Inference of Topologies and Dynamical Processes over Graphs


Network science provides valuable insights across
numerous disciplines including sociology, biology, neuroscience
and engineering. A task of major practical importance in these
application domains is inferring the network structure from
noisy observations at a subset of nodes. Available methods for
topology inference typically assume that the process over the
network is observed at all nodes. However, application-specific
constraints may prevent acquiring network-wide observations.

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Authors:
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis
Submitted On:
29 May 2018 - 1:31pm
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dsw_viysgg_18.pdf

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[1] Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis, "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3214. Accessed: Mar. 21, 2019.
@article{3214-18,
url = {http://sigport.org/3214},
author = {Vassilis N. Ioannidis; Yanning Shen; Georgios B. Giannakis },
publisher = {IEEE SigPort},
title = {Semi-Blind Inference of Topologies and Dynamical Processes over Graphs},
year = {2018} }
TY - EJOUR
T1 - Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
AU - Vassilis N. Ioannidis; Yanning Shen; Georgios B. Giannakis
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3214
ER -
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis. (2018). Semi-Blind Inference of Topologies and Dynamical Processes over Graphs. IEEE SigPort. http://sigport.org/3214
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis, 2018. Semi-Blind Inference of Topologies and Dynamical Processes over Graphs. Available at: http://sigport.org/3214.
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis. (2018). "Semi-Blind Inference of Topologies and Dynamical Processes over Graphs." Web.
1. Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis. Semi-Blind Inference of Topologies and Dynamical Processes over Graphs [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3214

False Discovery Rate Control with Concave Penalties using Stability Selection


False discovery rate (FDR) control is highly desirable in several high-dimensional estimation problems. While solving such problems, it is observed that traditional approaches such as the Lasso select a high number of false positives, which increase with higher noise and correlation levels in the dataset. Stability selection is a procedure which uses randomization with the Lasso to reduce the number of false positives.

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Authors:
Kush R. Varshney
Submitted On:
29 May 2018 - 1:22pm
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Poster for FDR Control with Concave Penalties using Stability Selection

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[1] Kush R. Varshney, "False Discovery Rate Control with Concave Penalties using Stability Selection", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3213. Accessed: Mar. 21, 2019.
@article{3213-18,
url = {http://sigport.org/3213},
author = {Kush R. Varshney },
publisher = {IEEE SigPort},
title = {False Discovery Rate Control with Concave Penalties using Stability Selection},
year = {2018} }
TY - EJOUR
T1 - False Discovery Rate Control with Concave Penalties using Stability Selection
AU - Kush R. Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3213
ER -
Kush R. Varshney. (2018). False Discovery Rate Control with Concave Penalties using Stability Selection. IEEE SigPort. http://sigport.org/3213
Kush R. Varshney, 2018. False Discovery Rate Control with Concave Penalties using Stability Selection. Available at: http://sigport.org/3213.
Kush R. Varshney. (2018). "False Discovery Rate Control with Concave Penalties using Stability Selection." Web.
1. Kush R. Varshney. False Discovery Rate Control with Concave Penalties using Stability Selection [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3213

On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization


Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original formulation of graph-based SSL and prove the supermodularity of an AG-SSL objective function under a broad class of regularization functions parameterized by Stieltjes matrices.

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Authors:
Pin-Yu Chen, Dennis Wei
Submitted On:
20 April 2018 - 12:31am
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[1] Pin-Yu Chen, Dennis Wei, "On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3067. Accessed: Mar. 21, 2019.
@article{3067-18,
url = {http://sigport.org/3067},
author = {Pin-Yu Chen; Dennis Wei },
publisher = {IEEE SigPort},
title = {On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization},
year = {2018} }
TY - EJOUR
T1 - On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization
AU - Pin-Yu Chen; Dennis Wei
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3067
ER -
Pin-Yu Chen, Dennis Wei. (2018). On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization. IEEE SigPort. http://sigport.org/3067
Pin-Yu Chen, Dennis Wei, 2018. On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization. Available at: http://sigport.org/3067.
Pin-Yu Chen, Dennis Wei. (2018). "On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization." Web.
1. Pin-Yu Chen, Dennis Wei. On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3067

THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE

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Submitted On:
19 April 2018 - 10:46pm
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Slides-ChordDivergence18April2018.pdf

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[1] , "THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3059. Accessed: Mar. 21, 2019.
@article{3059-18,
url = {http://sigport.org/3059},
author = { },
publisher = {IEEE SigPort},
title = {THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE},
year = {2018} }
TY - EJOUR
T1 - THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3059
ER -
. (2018). THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE. IEEE SigPort. http://sigport.org/3059
, 2018. THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE. Available at: http://sigport.org/3059.
. (2018). "THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE." Web.
1. . THE CHORD GAP DIVERGENCE AND A GENERALIZATION OF THE BHATTACHARYYA DISTANCE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3059

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