Boosted Inductive Matrix Completion for Image Tagging

Non-Linear Matrix Completion for Social Image Tagging

IEEE Access ◽

10.1109/access.2016.2624267 ◽

2017 ◽

Vol 5 ◽

pp. 6688-6696 ◽

Cited By ~ 14

Author(s):

Xing Xu ◽

Li He ◽

Huimin Lu ◽

Atsushi Shimada ◽

Rin-Ichiro Taniguchi

Keyword(s):

Matrix Completion ◽

Linear Matrix ◽

Social Image ◽

Image Tagging ◽

Non Linear

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Cross-Domain Relationship Prediction by Efficient Block Matrix Completion for Social Media Applications

International Journal of Performability Engineering ◽

10.23940/ijpe.20.07.p11.10871094 ◽

2020 ◽

Vol 16 (7) ◽

pp. 1087

Author(s):

Xiao Lizhi ◽

Zhang Zheng ◽

Sun Peng

Keyword(s):

Social Media ◽

Matrix Completion ◽

Block Matrix ◽

Cross Domain ◽

Media Applications

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Distant Supervision for Relation Extraction with Matrix Completion

10.3115/v1/p14-1079 ◽

2014 ◽

Cited By ~ 14

Author(s):

Miao Fan ◽

Deli Zhao ◽

Qiang Zhou ◽

Zhiyuan Liu ◽

Thomas Fang Zheng ◽

...

Keyword(s):

Matrix Completion ◽

Relation Extraction ◽

Distant Supervision

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Model and Method for Contributor’s Quality Assessment in Community Image Tagging Systems

Information and Control Systems ◽

10.31799/1684-8853-2018-4-45-51 ◽

2018 ◽

pp. 45-51

Author(s):

A. V. Ponomarev

Keyword(s):

Large Scale ◽

Wide Spectrum ◽

Preference Relation ◽

Pairwise Comparison ◽

Ground Truth ◽

Comparison Method ◽

Characteristic Matrix ◽

Image Tagging ◽

Proposed Model

Introduction: Large-scale human-computer systems involving people of various skills and motivation into the information processing process are currently used in a wide spectrum of applications. An acute problem in such systems is assessing the expected quality of each contributor; for example, in order to penalize incompetent or inaccurate ones and to promote diligent ones.Purpose: To develop a method of assessing the expected contributor’s quality in community tagging systems. This method should only use generally unreliable and incomplete information provided by contributors (with ground truth tags unknown).Results:A mathematical model is proposed for community image tagging (including the model of a contributor), along with a method of assessing the expected contributor’s quality. The method is based on comparing tag sets provided by different contributors for the same images, being a modification of pairwise comparison method with preference relation replaced by a special domination characteristic. Expected contributors’ quality is evaluated as a positive eigenvector of a pairwise domination characteristic matrix. Community tagging simulation has confirmed that the proposed method allows you to adequately estimate the expected quality of community tagging system contributors (provided that the contributors' behavior fits the proposed model).Practical relevance: The obtained results can be used in the development of systems based on coordinated efforts of community (primarily, community tagging systems).

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