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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 201–250 of 256 papers

TitleStatusHype
A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks—0
A Novel Random Forest Dissimilarity Measure for Multi-View Learning—0
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection—0
Approaching human 3D shape perception with neurally mappable models—0
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging—0
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity—0
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction—0
A Survey on Multi-View Clustering—0
A Survey on Multi-view Learning—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Attentive Convolutional Neural Network based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech—0
A unified framework based on graph consensus term for multi-view learning—0
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning—0
Auto-Encoder based Co-Training Multi-View Representation Learning—0
Auto-weighted Multi-view Feature Selection with Graph Optimization—0
Bayesian multi-tensor factorization—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview—0
Canonical Correlation Analysis with Implicit Distributions—0
Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers—0
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs—0
Deep Multi-View Learning for Tire Recommendation—0
Deep Multi-view Learning to Rank—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Deep Partial Multi-View Learning—0
Deep Transfer Tensor Factorization for Multi-View Learning—0
Deep Variational Canonical Correlation Analysis—0
Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations—0
Dissimilarity-based representation for radiomics applications—0
Diverse and Consistent Multi-view Networks for Semi-supervised Regression—0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
Dynamic voting in multi-view learning for radiomics applications—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—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
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
A smoothed-Bayesian approach to frequency recovery from sketched data—0
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Group-sparse Embeddings in Collective Matrix Factorization—0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)—0
Heterogeneous Representation Learning: A Review—0
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