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Machine Learning for Signal Processing

Bayesian inference for multi-line spectra in linear sensor array

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19 April 2018 - 6:39pm
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VietHung_ICASSP_2018.pdf

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[1] , "Bayesian inference for multi-line spectra in linear sensor array", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2910. Accessed: Apr. 25, 2018.
@article{2910-18,
url = {http://sigport.org/2910},
author = { },
publisher = {IEEE SigPort},
title = {Bayesian inference for multi-line spectra in linear sensor array},
year = {2018} }
TY - EJOUR
T1 - Bayesian inference for multi-line spectra in linear sensor array
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2910
ER -
. (2018). Bayesian inference for multi-line spectra in linear sensor array. IEEE SigPort. http://sigport.org/2910
, 2018. Bayesian inference for multi-line spectra in linear sensor array. Available at: http://sigport.org/2910.
. (2018). "Bayesian inference for multi-line spectra in linear sensor array." Web.
1. . Bayesian inference for multi-line spectra in linear sensor array [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2910

Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework

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KRISHAN SHARMA, SHIKHA GUPTA, DILEEP A.D, RENU RAMESHAN
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15 April 2018 - 12:22pm
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ICASSP_3200.pdf

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[1] KRISHAN SHARMA, SHIKHA GUPTA, DILEEP A.D, RENU RAMESHAN, "Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2897. Accessed: Apr. 25, 2018.
@article{2897-18,
url = {http://sigport.org/2897},
author = {KRISHAN SHARMA; SHIKHA GUPTA; DILEEP A.D; RENU RAMESHAN },
publisher = {IEEE SigPort},
title = {Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework},
year = {2018} }
TY - EJOUR
T1 - Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework
AU - KRISHAN SHARMA; SHIKHA GUPTA; DILEEP A.D; RENU RAMESHAN
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2897
ER -
KRISHAN SHARMA, SHIKHA GUPTA, DILEEP A.D, RENU RAMESHAN. (2018). Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework. IEEE SigPort. http://sigport.org/2897
KRISHAN SHARMA, SHIKHA GUPTA, DILEEP A.D, RENU RAMESHAN, 2018. Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework. Available at: http://sigport.org/2897.
KRISHAN SHARMA, SHIKHA GUPTA, DILEEP A.D, RENU RAMESHAN. (2018). "Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework." Web.
1. KRISHAN SHARMA, SHIKHA GUPTA, DILEEP A.D, RENU RAMESHAN. Scene Image Classification using ReducedVirtual Feature Representation in Sparse Framework [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2897

An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation


This paper presents a novel problem of detection and localization of anomalous events due to a certain class of objects in video data with applications to smart surveillance. A baseline system is proposed that uses a convolutional neural network (CNN) to generate pixel level masks corresponding to objects of a class of interest. A Restricted Boltzmann Machine (RBM) is then trained on the mask to learn patterns of normal behavior. The free energy of the RBM is used to detect the presence of an anomaly while the reconstruction error is used to localize the anomaly.

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Authors:
Burhan Ahmad Mudassar, Jong Hwan Ko, Saibal Mukhopadhyay
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16 April 2018 - 11:54am
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ICASSP 2018 Poster.pptx

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[1] Burhan Ahmad Mudassar, Jong Hwan Ko, Saibal Mukhopadhyay, "An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2895. Accessed: Apr. 25, 2018.
@article{2895-18,
url = {http://sigport.org/2895},
author = {Burhan Ahmad Mudassar; Jong Hwan Ko; Saibal Mukhopadhyay },
publisher = {IEEE SigPort},
title = {An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation},
year = {2018} }
TY - EJOUR
T1 - An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation
AU - Burhan Ahmad Mudassar; Jong Hwan Ko; Saibal Mukhopadhyay
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2895
ER -
Burhan Ahmad Mudassar, Jong Hwan Ko, Saibal Mukhopadhyay. (2018). An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation. IEEE SigPort. http://sigport.org/2895
Burhan Ahmad Mudassar, Jong Hwan Ko, Saibal Mukhopadhyay, 2018. An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation. Available at: http://sigport.org/2895.
Burhan Ahmad Mudassar, Jong Hwan Ko, Saibal Mukhopadhyay. (2018). "An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation." Web.
1. Burhan Ahmad Mudassar, Jong Hwan Ko, Saibal Mukhopadhyay. An Unsupervised Anomalous Event Detection Framework with Class-Aware Source Separation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2895

TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS

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Authors:
Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos
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14 April 2018 - 1:07pm
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Presentation_ICASSP.pdf

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[1] Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos, "TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2842. Accessed: Apr. 25, 2018.
@article{2842-18,
url = {http://sigport.org/2842},
author = {Konstantinos Makantasis; Anastasios Doulamis; Nikolaos Doulamis; Antonis Nikitakis; Athanasios Voulodimos },
publisher = {IEEE SigPort},
title = {TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS
AU - Konstantinos Makantasis; Anastasios Doulamis; Nikolaos Doulamis; Antonis Nikitakis; Athanasios Voulodimos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2842
ER -
Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos. (2018). TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS. IEEE SigPort. http://sigport.org/2842
Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos, 2018. TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS. Available at: http://sigport.org/2842.
Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos. (2018). "TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS." Web.
1. Konstantinos Makantasis, Anastasios Doulamis, Nikolaos Doulamis, Antonis Nikitakis, Athanasios Voulodimos. TENSOR-BASED NONLINEAR CLASSIFIER FOR HIGH-ORDER DATA ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2842

Crime incidents embedding using Restricted Boltzmann machine


We present a new approach for detecting related crime series, by unsupervised learning of the latent feature embeddings from narratives of crime record via the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a drastically different approach from prior work on crime analysis, which typically considers only time and location and at most category information.

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Authors:
Shixiang Zhu, Yao Xie
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14 April 2018 - 12:16am
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ICASSP-Slides.pdf

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[1] Shixiang Zhu, Yao Xie, "Crime incidents embedding using Restricted Boltzmann machine", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2793. Accessed: Apr. 25, 2018.
@article{2793-18,
url = {http://sigport.org/2793},
author = {Shixiang Zhu; Yao Xie },
publisher = {IEEE SigPort},
title = {Crime incidents embedding using Restricted Boltzmann machine},
year = {2018} }
TY - EJOUR
T1 - Crime incidents embedding using Restricted Boltzmann machine
AU - Shixiang Zhu; Yao Xie
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2793
ER -
Shixiang Zhu, Yao Xie. (2018). Crime incidents embedding using Restricted Boltzmann machine. IEEE SigPort. http://sigport.org/2793
Shixiang Zhu, Yao Xie, 2018. Crime incidents embedding using Restricted Boltzmann machine. Available at: http://sigport.org/2793.
Shixiang Zhu, Yao Xie. (2018). "Crime incidents embedding using Restricted Boltzmann machine." Web.
1. Shixiang Zhu, Yao Xie. Crime incidents embedding using Restricted Boltzmann machine [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2793

MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING


The rapid rise of IoT and Big Data can facilitate the use of data to enhance our quality of life. However, the omnipresent and sensitive nature of data can simultaneously generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve the intended purposes, but not for prying into one’s sensitive information. We address this challenge via utility maximizing lossy compression of data.

Poster.pdf

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Authors:
Mert Al, Thee Chanyaswad, Sun-Yuan Kung
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13 April 2018 - 3:55pm
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[1] Mert Al, Thee Chanyaswad, Sun-Yuan Kung, "MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2756. Accessed: Apr. 25, 2018.
@article{2756-18,
url = {http://sigport.org/2756},
author = {Mert Al; Thee Chanyaswad; Sun-Yuan Kung },
publisher = {IEEE SigPort},
title = {MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING},
year = {2018} }
TY - EJOUR
T1 - MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING
AU - Mert Al; Thee Chanyaswad; Sun-Yuan Kung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2756
ER -
Mert Al, Thee Chanyaswad, Sun-Yuan Kung. (2018). MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING. IEEE SigPort. http://sigport.org/2756
Mert Al, Thee Chanyaswad, Sun-Yuan Kung, 2018. MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING. Available at: http://sigport.org/2756.
Mert Al, Thee Chanyaswad, Sun-Yuan Kung. (2018). "MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING." Web.
1. Mert Al, Thee Chanyaswad, Sun-Yuan Kung. MULTI-KERNEL, DEEP NEURAL NETWORK AND HYBRID MODELS FOR PRIVACY PRESERVING MACHINE LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2756

OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION


Pattern recognition on big data can be challenging for kernel machines as the complexity grows with the squared number of training samples. In this work, we overcome this hurdle via the outlying data sample removal pre-processing step. This approach removes less informative data samples and trains the kernel machines only with the remaining data, and hence, directly reduces the complexity by reducing the number of training samples. To enhance the classification performance, the outlier removal process is done such that the discriminant information of the data is mostly intact.

ordi2.pdf

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Authors:
Thee Chanyaswad, Mert Al, Sun-Yuan Kung
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14 April 2018 - 9:10pm
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ordi2.pdf

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[1] Thee Chanyaswad, Mert Al, Sun-Yuan Kung, "OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2754. Accessed: Apr. 25, 2018.
@article{2754-18,
url = {http://sigport.org/2754},
author = {Thee Chanyaswad; Mert Al; Sun-Yuan Kung },
publisher = {IEEE SigPort},
title = {OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION},
year = {2018} }
TY - EJOUR
T1 - OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION
AU - Thee Chanyaswad; Mert Al; Sun-Yuan Kung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2754
ER -
Thee Chanyaswad, Mert Al, Sun-Yuan Kung. (2018). OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION. IEEE SigPort. http://sigport.org/2754
Thee Chanyaswad, Mert Al, Sun-Yuan Kung, 2018. OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION. Available at: http://sigport.org/2754.
Thee Chanyaswad, Mert Al, Sun-Yuan Kung. (2018). "OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION." Web.
1. Thee Chanyaswad, Mert Al, Sun-Yuan Kung. OUTLIER REMOVAL FOR ENHANCING KERNEL-BASED CLASSIFIER VIA THE DISCRIMINANT INFORMATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2754

ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS


In this work we propose a new adaptive algorithm for coop- erative spectrum sensing in dynamic environments where the channels are time varying. We assume a centralized spectrum sensing procedure based on the soft fusion of the signal energy levels measured at the sensors. The detection problem is posed as a composite hypothesis testing problem. The unknown pa- rameters are estimated by means of an adaptive clustering al- gorithm that operates over the most recent energy estimates re- ported by the sensors to the fusion center.

poster.pdf

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Authors:
Ignacio Santamaria
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13 April 2018 - 2:41pm
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[1] Ignacio Santamaria, "ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2730. Accessed: Apr. 25, 2018.
@article{2730-18,
url = {http://sigport.org/2730},
author = {Ignacio Santamaria },
publisher = {IEEE SigPort},
title = {ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS
AU - Ignacio Santamaria
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2730
ER -
Ignacio Santamaria. (2018). ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS. IEEE SigPort. http://sigport.org/2730
Ignacio Santamaria, 2018. ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS. Available at: http://sigport.org/2730.
Ignacio Santamaria. (2018). "ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS." Web.
1. Ignacio Santamaria. ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2730

Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks


In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i.e. transferring) knowledge from sensor data and enhancing low-resolution target detection. In unconstrained surveillance settings, sensor measurements are often noisy, degraded, corrupted, and even missing/absent, thereby presenting a significant problem for multi-modal fusion. We therefore specifically tackle the problem of a missing modality in our attempt to propose an algorithm

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Authors:
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai
Submitted On:
13 April 2018 - 12:56pm
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[1] Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai, "Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2729. Accessed: Apr. 25, 2018.
@article{2729-18,
url = {http://sigport.org/2729},
author = {Siddharth Roheda; Benjamin S Riggan; Hamid Krim; Liyi Dai },
publisher = {IEEE SigPort},
title = {Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks},
year = {2018} }
TY - EJOUR
T1 - Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks
AU - Siddharth Roheda; Benjamin S Riggan; Hamid Krim; Liyi Dai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2729
ER -
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai. (2018). Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks. IEEE SigPort. http://sigport.org/2729
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai, 2018. Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks. Available at: http://sigport.org/2729.
Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai. (2018). "Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks." Web.
1. Siddharth Roheda, Benjamin S Riggan, Hamid Krim, Liyi Dai. Cross-Modality Distillation: A Case for Conditional Generative Adversarial Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2729

DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING

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Authors:
Jing Li, Feiping Nie, Xuelong Li
Submitted On:
13 April 2018 - 10:58am
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ICASSP 2018.pdf

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[1] Jing Li, Feiping Nie, Xuelong Li, "DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2714. Accessed: Apr. 25, 2018.
@article{2714-18,
url = {http://sigport.org/2714},
author = {Jing Li; Feiping Nie; Xuelong Li },
publisher = {IEEE SigPort},
title = {DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING},
year = {2018} }
TY - EJOUR
T1 - DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING
AU - Jing Li; Feiping Nie; Xuelong Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2714
ER -
Jing Li, Feiping Nie, Xuelong Li. (2018). DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING. IEEE SigPort. http://sigport.org/2714
Jing Li, Feiping Nie, Xuelong Li, 2018. DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING. Available at: http://sigport.org/2714.
Jing Li, Feiping Nie, Xuelong Li. (2018). "DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING." Web.
1. Jing Li, Feiping Nie, Xuelong Li. DIRECTLY SOLVING THE ORIGINAL RATIOCUT PROBLEM FOR EFFECTIVE DATA CLUSTERING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2714

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