MULTI-VIEW LEARNING
51 papers with code • 0 benchmarks • 1 datasets
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
Benchmarks
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Libraries
Use these libraries to find MULTI-VIEW LEARNING models and implementations
CarFusion
Latest papers
Siamese DETR
zx55/siamesedetr
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CVPR 2023
In this work, we present Siamese DETR, a Siamese self-supervised pretraining approach for the Transformer architecture in DETR.
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing Views
justsmart/DIMC-mindspore
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IEEE Transactions on Neural Networks and Learning Systems 2023
View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery.
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
A Clustering-guided Contrastive Fusion for Multi-view Representation Learning
To this end, we utilize a deep fusion network to fuse view-specific representations into the view-common representation, extracting high-level semantics for obtaining robust representation.
Syntactic Multi-view Learning for Open Information Extraction
In this paper, we model both constituency and dependency trees into word-level graphs, and enable neural OpenIE to learn from the syntactic structures.
Heterogeneous Graph Contrastive Multi-view Learning
How to mitigate the sampling bias for heterogeneous GCL is another important problem.
Geolocation of Cultural Heritage using Multi-View Knowledge Graph Embedding
Knowledge Graphs (KGs) have proven to be a reliable way of structuring data.
Localized Sparse Incomplete Multi-view Clustering
Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation.
Variational Distillation for Multi-View Learning
Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions.
Trusted Multi-View Classification with Dynamic Evidential Fusion
hanmenghan/TMC
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With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
