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MULTI-VIEW LEARNING

Multi-View Learning is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.

Source: Dissimilarity-based representation for radiomics applications

Papers

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Showing 76–100 of 256 papers

TitleStatusHype
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning—0
A Comparison of Multi-View Learning Strategies for Satellite Image-Based Real Estate Appraisal—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Dynamic voting in multi-view learning for radiomics applications—0
A Multi-view Perspective of Self-supervised Learning—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Bayesian multi-tensor factorization—0
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning—0
Diverse and Consistent Multi-view Networks for Semi-supervised Regression—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
Anomaly detecting and ranking of the cloud computing platform by multi-view learning—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Factorized Latent Spaces with Structured Sparsity—0
A unified framework based on graph consensus term for multi-view learning—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers—0
Dissimilarity-based representation for radiomics applications—0
A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks—0
A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection—0
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