Semi-supervised EEG emotion recognition model based on enhanced graph fusion and GCN
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ac63ec
Abstract: Objective. To take full advantage of both labeled data and unlabeled ones, the Graph Convolutional Network (GCN) was introduced in electroencephalography (EEG) based emotion recognition to achieve feature propagation. However, a single feature cannot represent… read more here.
Keywords: fusion; feature; graph fusion; seed ... See more keywords
Hyperspectral and LiDAR Classification With Semisupervised Graph Fusion
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2019.2928009
Abstract: To fuse hyperspectral and Light Detection And Ranging (LiDAR), we propose a semisupervised graph fusion (SSGF) approach. We apply morphological filters to LiDAR and the first few components of hyperspectral data to model the height… read more here.
Keywords: semisupervised graph; lidar; fusion; hyperspectral lidar ... See more keywords
Incomplete Multi-View Clustering With Sample-Level Auto-Weighted Graph Fusion
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3171911
Abstract: Incomplete multi-view clustering (IMC) has received considerable attention due to its flexibility in fusing the multi-view information when the view samples are partly missing. However, existing methods seldom consider the affection of the missing samples… read more here.
Keywords: view; sample level; multi view; graph fusion ... See more keywords