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Found 8 papers, 0 papers with code
CvFormer: Cross-view transFormers with Pre-training for fMRI Analysis of Human Brain
In recent years, functional magnetic resonance imaging (fMRI) has been widely utilized to diagnose neurological disease, by exploiting the region of interest (RoI) nodes as well as their connectivities in human brain.
TiBGL: Template-induced Brain Graph Learning for Functional Neuroimaging Analysis
Motivated by the related medical findings on functional connectivites, TiBGL proposes template-induced brain graph learning to extract template brain graphs for all groups.
Locality Relationship Constrained Multi-view Clustering Framework
LRC-MCF aims to explore the diversity, geometric, consensus and complementary information among different views, by capturing the locality relationship information and the common similarity relationships among multiple views.
A unified framework based on graph consensus term for multi-view learning
In this paper, we propose a novel multi-view learning framework, which aims to leverage most existing graph embedding works into a unified formula via introducing the graph consensus term.
Multimodal-Aware Weakly Supervised Metric Learning with Self-weighting Triplet Loss
Therefore, how to learn an appropriate distance metric from weakly supervised data remains an open but challenging problem.
Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation
Unlike existing methods with additive parameters, the proposed method could automatically allocate a suitable weight for each view in multi-view information fusion.
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction
Two schemes based on pairwise-consensus and centroid-consensus are separately proposed to force multiple views to learn from each other and then an iterative alternating strategy is developed to obtain the optimal solution.
Multi-view Locality Low-rank Embedding for Dimension Reduction
Even though most of them can achieve satisfactory performance in some certain situations, they fail to fully consider the information from multiple views which are highly relevant but sometimes look different from each other.
