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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
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss—0
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions—0
Multi-view Regularized Gaussian Processes—0
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants—0
Multi-View representation learning in Multi-Task Scene—0
Multi-view Sentence Representation Learning—0
Multi-view Subspace Adaptive Learning via Autoencoder and Attention—0
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment—0
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation—0
Neural News Recommendation with Heterogeneous User Behavior—0
One Size Fits Many: Column Bundle for Multi-X Learning—0
Explainable Multi-View Deep Networks Methodology for Experimental PhysicsCode0
Dynamic Evidence Decoupling for Trusted Multi-view LearningCode0
Dual Memory Neural Computer for Asynchronous Two-view Sequential LearningCode0
URL: Universal Referential Knowledge Linking via Task-instructed Representation CompressionCode0
Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack DetectionCode0
Balanced Multi-view ClusteringCode0
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view DataCode0
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing ViewsCode0
Syntactic Multi-view Learning for Open Information ExtractionCode0
Patterns for Learning with Side InformationCode0
Bundle Recommendation with Item-level Causation-enhanced Multi-view LearningCode0
Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion AggregationCode0
Semantic Invariant Multi-view Clustering with Fully Incomplete InformationCode0
Geolocation of Cultural Heritage using Multi-View Knowledge Graph EmbeddingCode0
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