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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 201–225 of 256 papers

TitleStatusHype
Anomaly detecting and ranking of the cloud computing platform by multi-view learning—0
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
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