MULTI-VIEW LEARNING
52 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
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CarFusion
Latest papers with no code
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications
Finally, we apply the proposed GCSD to two challenging machine learning tasks, namely deep learning-based clustering and the problem of multi-source domain adaptation.
MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging
MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability.
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning
Multi-view learning has become a popular research topic in recent years, but research on the cross-application of classic multi-label classification and multi-view learning is still in its early stages.
Trusted Multi-view Learning with Label Noise
This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels?
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data
Deep learning models have proven to be effective for this task by mapping time series data to high-level representation for prediction.
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility
Urban Air Mobility (UAM) expands vehicles from the ground to the near-ground space, envisioned as a revolution for transportation systems.
Scalable Multi-view Clustering via Explicit Kernel Features Maps
A growing awareness of multi-view learning as an important component in data science and machine learning is a consequence of the increasing prevalence of multiple views in real-world applications, especially in the context of networks.
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction
The GU module learned different weights based on the country and crop-type, aligning with the variable significance of each data source to the prediction task.
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance.
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning
This paper presents a series of new results for domain adaptation in the multi-view learning setting.
