Latent Multi-view Semi-Supervised Classification
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.
Code (1)
Tasks
ClassificationGeneral Classificationgraph constructionGraph LearningRepresentation LearningSimilar Papers 제목 키워드 기반
Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data
There are threefold challenges in emotion recognition. First, it is difficult to recognize human's emotional states only considering a single modality. Second, it is expensive to manually annotate the emotional data. Thi…
Emotion RecognitionImputationMulti-View representation learning in Multi-Task Scene
Over recent decades have witnessed considerable progress in whether multi-task learning or multi-view learning, but the situation that consider both learning scenes simultaneously has received not too much attention. How…
Multi-Task LearningMULTI-VIEW LEARNINGRepresentation LearningMixed Graph Contrastive Network for Semi-Supervised Node Classification
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised node classification in recent years. However, the problem of insufficient supervision, together with representation collapse, largely li…
ClassificationContrastive LearningData AugmentationGraph Learning+2Semi-supervised Bayesian Deep Multi-modal Emotion Recognition
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly expensive. These two aspects make the bu…
Emotion RecognitionImputationDualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness
Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original in…
MULTI-VIEW LEARNINGNode ClassificationRepresentation Learning