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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 161–170 of 256 papers

TitleStatusHype
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
Learning Autoencoders with Relational RegularizationCode1
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
Dual Adversarial Domain AdaptationCode1
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