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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 151–175 of 256 papers

TitleStatusHype
Uncorrelated Semi-paired Subspace Learning—0
Deep Partial Multi-View Learning—0
Layer-Wise Multi-View Learning for Neural Machine Translation—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Random Forest for Dissimilarity-based Multi-view Learning—0
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning—0
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions—0
A Novel Random Forest Dissimilarity Measure for Multi-View Learning—0
Deep brain state classification of MEG dataCode0
A Tweet-based Dataset for Company-Level Stock Return PredictionCode0
Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation—0
Hierarchical Optimal Transport for Robust Multi-View Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction—0
Heterogeneous Representation Learning: A Review—0
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning—0
Variational Inference for Deep Probabilistic Canonical Correlation Analysis—0
Multi-View Learning for Vision-and-Language Navigation—0
A Multi-view Perspective of Self-supervised Learning—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection—0
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data—0
Towards Disentangled Representations for Human Retargeting by Multi-view Learning—0
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development—0
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