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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 101–125 of 256 papers

TitleStatusHype
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning—0
Imputation of missing values in multi-view data—0
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants—0
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation—0
Geolocation of Cultural Heritage using Multi-View Knowledge Graph EmbeddingCode0
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Adaptive incomplete multi-view learning via tensor graph completion—0
A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Alternately Optimized Graph Neural Networks—0
scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data—0
Multi-view Information Bottleneck Without Variational ApproximationCode0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
Asymmetric Proxy Loss for Multi-View Acoustic Word Embeddings—0
Deep Multi-View Learning for Tire Recommendation—0
Variational Interpretable Learning from Multi-view Data—0
Higher Order Correlation Analysis for Multi-View Learning—0
Multi-view learning with privileged weighted twin support vector machine—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Multi-View representation learning in Multi-Task Scene—0
Uncertainty-Aware Multi-View Representation Learning—0
Auto-Encoder based Co-Training Multi-View Representation Learning—0
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss—0
Multi-view Data Classification with a Label-driven Auto-weighted Strategy—0
Multi-view Subspace Adaptive Learning via Autoencoder and Attention—0
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