Sorry, you need to enable JavaScript to visit this website.

Knowledge and Data Engineering

An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition


In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications.

Paper Details

Authors:
Nuha Zamzami, and Nizar Bouguila
Submitted On:
9 November 2019 - 7:08am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

MSD_MeshPres.pdf

(8)

Subscribe

[1] Nuha Zamzami, and Nizar Bouguila , "An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4940. Accessed: Nov. 14, 2019.
@article{4940-19,
url = {http://sigport.org/4940},
author = {Nuha Zamzami; and Nizar Bouguila },
publisher = {IEEE SigPort},
title = {An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition},
year = {2019} }
TY - EJOUR
T1 - An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition
AU - Nuha Zamzami; and Nizar Bouguila
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4940
ER -
Nuha Zamzami, and Nizar Bouguila . (2019). An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition. IEEE SigPort. http://sigport.org/4940
Nuha Zamzami, and Nizar Bouguila , 2019. An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition. Available at: http://sigport.org/4940.
Nuha Zamzami, and Nizar Bouguila . (2019). "An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition." Web.
1. Nuha Zamzami, and Nizar Bouguila . An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4940

An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition


In this paper, we examine the problem of modeling overdispersed frequency vectors that are naturally generated by several machine learning and computer vision applications.

Paper Details

Authors:
Nuha Zamzami, and Nizar Bouguila
Submitted On:
9 November 2019 - 7:05am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

MSD_Mesh.pdf

(6)

Subscribe

[1] Nuha Zamzami, and Nizar Bouguila , "An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4939. Accessed: Nov. 14, 2019.
@article{4939-19,
url = {http://sigport.org/4939},
author = {Nuha Zamzami; and Nizar Bouguila },
publisher = {IEEE SigPort},
title = {An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition},
year = {2019} }
TY - EJOUR
T1 - An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition
AU - Nuha Zamzami; and Nizar Bouguila
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4939
ER -
Nuha Zamzami, and Nizar Bouguila . (2019). An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition. IEEE SigPort. http://sigport.org/4939
Nuha Zamzami, and Nizar Bouguila , 2019. An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition. Available at: http://sigport.org/4939.
Nuha Zamzami, and Nizar Bouguila . (2019). "An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition." Web.
1. Nuha Zamzami, and Nizar Bouguila . An Accurate Evaluation of MSD Log-likelihood and its Application in Human Action Recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4939

Fuzzy Personalized Scoring Model for Recommendation System


In this research, we aim to propose a data preprocessing framework particularly for financial sector to generate the rating data as input to the collaborative system. First, clustering technique is applied to cluster all users based on their demographic information which might be able to differentiate the customers’ background. Then, for each customer group, the importance of demographic characteristics which are highly associated with financial products purchasing are analyzed by the proposed fuzzy integral technique.

Paper Details

Authors:
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng
Submitted On:
8 May 2019 - 7:40am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster_design_20190508_final.pdf

(36)

Subscribe

[1] Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng, "Fuzzy Personalized Scoring Model for Recommendation System", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4077. Accessed: Nov. 14, 2019.
@article{4077-19,
url = {http://sigport.org/4077},
author = {Chao-Lung Yang; Shang-Che Hsu; Kai-Lung Hua; Wen-Huang Cheng },
publisher = {IEEE SigPort},
title = {Fuzzy Personalized Scoring Model for Recommendation System},
year = {2019} }
TY - EJOUR
T1 - Fuzzy Personalized Scoring Model for Recommendation System
AU - Chao-Lung Yang; Shang-Che Hsu; Kai-Lung Hua; Wen-Huang Cheng
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4077
ER -
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng. (2019). Fuzzy Personalized Scoring Model for Recommendation System. IEEE SigPort. http://sigport.org/4077
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng, 2019. Fuzzy Personalized Scoring Model for Recommendation System. Available at: http://sigport.org/4077.
Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng. (2019). "Fuzzy Personalized Scoring Model for Recommendation System." Web.
1. Chao-Lung Yang, Shang-Che Hsu, Kai-Lung Hua, Wen-Huang Cheng. Fuzzy Personalized Scoring Model for Recommendation System [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4077

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.

Paper Details

Authors:
N.D. Sidiropoulos, N. Kargas, X. Fu
Submitted On:
24 December 2018 - 8:25pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

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

(149)

Subscribe

[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: Nov. 14, 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

Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series


Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes.

Paper Details

Authors:
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano
Submitted On:
1 March 2019 - 9:08pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Dynamic network identification poster

(91)

Keywords

Additional Categories

Subscribe

[1] Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3841. Accessed: Nov. 14, 2019.
@article{3841-18,
url = {http://sigport.org/3841},
author = {Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano },
publisher = {IEEE SigPort},
title = {Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series},
year = {2018} }
TY - EJOUR
T1 - Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series
AU - Daniel Romero; Bakht Zaman; Baltasar Beferull-Lozano
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3841
ER -
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. IEEE SigPort. http://sigport.org/3841
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano, 2018. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series. Available at: http://sigport.org/3841.
Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. (2018). "Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series." Web.
1. Daniel Romero, Bakht Zaman, Baltasar Beferull-Lozano. Dynamic Network Identification From Non-stationary Vector Autoregressive Time Series [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3841

Diversity in Fashion Recommendation Using Semantic Parsing


Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing works for fashion recommendation have used Siamese or Triplet network to learn features between a similar pair and a similar dissimilar triplet respectively.

Paper Details

Authors:
Sukhad Anand, Chetan Arora, Atul Rai
Submitted On:
8 October 2018 - 4:26am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Fashion recommendation based on contextual similarity

(94)

Subscribe

[1] Sukhad Anand, Chetan Arora, Atul Rai, "Diversity in Fashion Recommendation Using Semantic Parsing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3622. Accessed: Nov. 14, 2019.
@article{3622-18,
url = {http://sigport.org/3622},
author = {Sukhad Anand; Chetan Arora; Atul Rai },
publisher = {IEEE SigPort},
title = {Diversity in Fashion Recommendation Using Semantic Parsing},
year = {2018} }
TY - EJOUR
T1 - Diversity in Fashion Recommendation Using Semantic Parsing
AU - Sukhad Anand; Chetan Arora; Atul Rai
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3622
ER -
Sukhad Anand, Chetan Arora, Atul Rai. (2018). Diversity in Fashion Recommendation Using Semantic Parsing. IEEE SigPort. http://sigport.org/3622
Sukhad Anand, Chetan Arora, Atul Rai, 2018. Diversity in Fashion Recommendation Using Semantic Parsing. Available at: http://sigport.org/3622.
Sukhad Anand, Chetan Arora, Atul Rai. (2018). "Diversity in Fashion Recommendation Using Semantic Parsing." Web.
1. Sukhad Anand, Chetan Arora, Atul Rai. Diversity in Fashion Recommendation Using Semantic Parsing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3622

Randomized Sampling-based Fly Local Sensitive Hashing


Fly Local Sensitive Hashing (FLSH) is a biomimetic data-independent hashing method inspired by the mechanism of odor processing system in drosophila. In this paper,we propose a novel Randomized Sampling-based Fly Local Sensitive Hashing (rs-FLSH) to model the randomness occurred during the establishment of synapses between neurons.Significant performance improvement can be achieved by applying a novel randomized sampling scheme in rs-FLSH,in which the sample rate is modeled by a Gaussian random variable rather than a fixed value in FLSH.

Paper Details

Authors:
Yu QIAO
Submitted On:
4 October 2018 - 9:49pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICIP Poster.pdf

(137)

Subscribe

[1] Yu QIAO, "Randomized Sampling-based Fly Local Sensitive Hashing", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3475. Accessed: Nov. 14, 2019.
@article{3475-18,
url = {http://sigport.org/3475},
author = {Yu QIAO },
publisher = {IEEE SigPort},
title = {Randomized Sampling-based Fly Local Sensitive Hashing},
year = {2018} }
TY - EJOUR
T1 - Randomized Sampling-based Fly Local Sensitive Hashing
AU - Yu QIAO
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3475
ER -
Yu QIAO. (2018). Randomized Sampling-based Fly Local Sensitive Hashing. IEEE SigPort. http://sigport.org/3475
Yu QIAO, 2018. Randomized Sampling-based Fly Local Sensitive Hashing. Available at: http://sigport.org/3475.
Yu QIAO. (2018). "Randomized Sampling-based Fly Local Sensitive Hashing." Web.
1. Yu QIAO. Randomized Sampling-based Fly Local Sensitive Hashing [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3475

Alternating autoencoders for matrix completion


We consider autoencoders (AEs) for matrix completion (MC) with application to collaborative filtering (CF) for recommedation systems. It is observed that for a given sparse user-item rating matrix, denoted asM, an AE performs matrix factorization so that the recovered matrix is represented as a product of user and item feature matrices.

Paper Details

Authors:
Kiwon Lee, Yong H. Lee, Changho Suh
Submitted On:
4 June 2018 - 2:48pm
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

Poster_Lee.pdf

(154)

Subscribe

[1] Kiwon Lee, Yong H. Lee, Changho Suh, "Alternating autoencoders for matrix completion", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3223. Accessed: Nov. 14, 2019.
@article{3223-18,
url = {http://sigport.org/3223},
author = {Kiwon Lee; Yong H. Lee; Changho Suh },
publisher = {IEEE SigPort},
title = {Alternating autoencoders for matrix completion},
year = {2018} }
TY - EJOUR
T1 - Alternating autoencoders for matrix completion
AU - Kiwon Lee; Yong H. Lee; Changho Suh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3223
ER -
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). Alternating autoencoders for matrix completion. IEEE SigPort. http://sigport.org/3223
Kiwon Lee, Yong H. Lee, Changho Suh, 2018. Alternating autoencoders for matrix completion. Available at: http://sigport.org/3223.
Kiwon Lee, Yong H. Lee, Changho Suh. (2018). "Alternating autoencoders for matrix completion." Web.
1. Kiwon Lee, Yong H. Lee, Changho Suh. Alternating autoencoders for matrix completion [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3223

Profit Maximizing Logistic Regression Modeling for Credit Scoring


Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model.

Paper Details

Authors:
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme
Submitted On:
30 May 2018 - 8:30pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

PosterA1_Arnout_Devos_DSW2018.pdf

(158)

Subscribe

[1] Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, "Profit Maximizing Logistic Regression Modeling for Credit Scoring", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3221. Accessed: Nov. 14, 2019.
@article{3221-18,
url = {http://sigport.org/3221},
author = {Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme },
publisher = {IEEE SigPort},
title = {Profit Maximizing Logistic Regression Modeling for Credit Scoring},
year = {2018} }
TY - EJOUR
T1 - Profit Maximizing Logistic Regression Modeling for Credit Scoring
AU - Arnout Devos; Jakob Dhondt; Eugen Stripling; Bart Baesens; Seppe vanden Broucke; Gaurav Sukhatme
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3221
ER -
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). Profit Maximizing Logistic Regression Modeling for Credit Scoring. IEEE SigPort. http://sigport.org/3221
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme, 2018. Profit Maximizing Logistic Regression Modeling for Credit Scoring. Available at: http://sigport.org/3221.
Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. (2018). "Profit Maximizing Logistic Regression Modeling for Credit Scoring." Web.
1. Arnout Devos, Jakob Dhondt, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Gaurav Sukhatme. Profit Maximizing Logistic Regression Modeling for Credit Scoring [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3221

PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS


In recent years, neural networks (NN) have achieved remarkable
performance improvement in text classification due to
their powerful ability to encode discriminative features by
incorporating label information into model training. Inspired
by the success of NN in text classification, we propose a
pseudo-supervised neural network approach for text clustering.
The neural network is trained in a supervised fashion
with pseudo-labels, which are provided by the cluster labels
of pre-clustering on unsupervised document representations.

Paper Details

Authors:
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling
Submitted On:
13 April 2018 - 3:58am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP-Chen Peixin_v2.pdf

(43)

Keywords

Additional Categories

Subscribe

[1] Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2640. Accessed: Nov. 14, 2019.
@article{2640-18,
url = {http://sigport.org/2640},
author = {Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling },
publisher = {IEEE SigPort},
title = {PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS},
year = {2018} }
TY - EJOUR
T1 - PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS
AU - Peixin Chen; Wu Guo; Lirong Dai; Zhenhua Ling
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2640
ER -
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. IEEE SigPort. http://sigport.org/2640
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling, 2018. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS. Available at: http://sigport.org/2640.
Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. (2018). "PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS." Web.
1. Peixin Chen, Wu Guo, Lirong Dai, Zhenhua Ling. PSEUDO-SUPERVISED APPROACH FOR TEXT CLUSTERING BASED ON CONSENSUS ANALYSIS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2640

Pages