most citedSupervised feature selection with orthogonal regression and feature weighting

3 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2025

FDC-Net: Rethinking the association between EEG artifact removal and multi-dimensional affective computing

Wenjia Dong, Xueyuan Xu, Tianze Yu +2

Electroencephalogram (EEG)-based emotion recognition holds significant value in affective computing and brain-computer interfaces. However, in practical applications, EEG recording…

cs.HC2025

ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection

Xueyuan Xu, Tianze Yu, Wenjia Dong +2

Recently, multi-modal physiological signals based emotion recognition has garnered increasing attention in the field of brain-computer interfaces. Nevertheness, the associated mult…

cs.HC2025

REFS: Robust EEG feature selection with missing multi-dimensional annotation for emotion recognition

Xueyuan Xu, Wenjia Dong, Fulin Wei +1

The affective brain-computer interface is a crucial technology for affective interaction and emotional intelligence, emerging as a significant area of research in the human-compute…

cs.HC2025

ADSEL: Adaptive dual self-expression learning for EEG feature selection via incomplete multi-dimensional emotional tagging

Tianze Yu, Junming Zhang, Wenjia Dong +2

EEG based multi-dimension emotion recognition has attracted substantial research interest in human computer interfaces. However, the high dimensionality of EEG features, coupled wi…

cs.HC2025

CWEFS: Brain volume conduction effects inspired channel-wise EEG feature selection for multi-dimensional emotion recognition

Xueyuan Xu, Wenjia Dong, Fulin Wei +1

Due to the intracranial volume conduction effects, high-dimensional multi-channel electroencephalography (EEG) features often contain substantial redundant and irrelevant informati…

cs.LG20193 cited

Supervised feature selection with orthogonal regression and feature weighting

Xia Wu, Xueyuan Xu, Jianhong Liu +3

Effective features can improve the performance of a model, which can thus help us understand the characteristics and underlying structure of complex data. Previous feature selectio…