scholarly journals Semi-supervised Multi-aspect Detection of Misinformation Using Hierarchical Joint Decomposition

2021 ◽  
pp. 406-422
Author(s):  
Sara Abdali ◽  
Neil Shah ◽  
Evangelos E. Papalexakis
Keyword(s):  
Joint Decomposition

scholarly journals Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

2020 ◽  
Vol 17 (4) ◽  
pp. 046018 ◽  
Author(s):  
Jennifer Stiso ◽  
Marie-Constance Corsi ◽  
Jean M Vettel ◽  
Javier Garcia ◽  
Fabio Pasqualetti ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Interface Control ◽  
Brain And Behavior ◽  
And Behavior ◽  
Joint Decomposition

scholarly journals An Ensemble Multilabel Classification for Disease Risk Prediction

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Runzhi Li ◽  
Wei Liu ◽  
Yusong Lin ◽  
Hongling Zhao ◽  
Chaoyang Zhang
Keyword(s):  
Risk Prediction ◽  
Disease Risk ◽  
Classification Problem ◽  
Decomposition Methods ◽  
Training Dataset ◽  
Multilabel Classification ◽  
Decomposition Strategies ◽  
Physical Examinations ◽  
Imbalance Learning ◽  
Joint Decomposition

It is important to identify and prevent disease risk as early as possible through regular physical examinations. We formulate the disease risk prediction into a multilabel classification problem. A novel Ensemble Label Power-set Pruned datasets Joint Decomposition (ELPPJD) method is proposed in this work. First, we transform the multilabel classification into a multiclass classification. Then, we propose the pruned datasets and joint decomposition methods to deal with the imbalance learning problem. Two strategies size balanced (SB) and label similarity (LS) are designed to decompose the training dataset. In the experiments, the dataset is from the real physical examination records. We contrast the performance of the ELPPJD method with two different decomposition strategies. Moreover, the comparison between ELPPJD and the classic multilabel classification methods RAkEL and HOMER is carried out. The experimental results show that the ELPPJD method with label similarity strategy has outstanding performance.

scholarly journals Stereo Video Quality Metric Based on Multi-Dimensional Analysis

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1129
Author(s):  
Zhouyan He ◽  
Haiyong Xu ◽  
Ting Luo ◽  
Yi Liu ◽  
Yang Song
Keyword(s):  
Dimensional Analysis ◽  
Video Quality ◽  
Low Frequency ◽  
Quality Metric ◽  
Stereo Video ◽  
Video Systems ◽  
Different Dimensions ◽  
Video Quality Metric ◽  
Energy Weighting ◽  
Joint Decomposition

Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs.

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