Self-paced co-training of graph neural networks for semi-supervised node classification
Graph neural networks (GNNs) have demonstrated great success in many graph data-based
applications. The impressive behavior of GNNs typically relies on the availability of a …
applications. The impressive behavior of GNNs typically relies on the availability of a …
Non-aligned multi-view multi-label classification via learning view-specific labels
D Zhao, Q Gao, Y Lu, D Sun - IEEE Transactions on Multimedia, 2022 - ieeexplore.ieee.org
In the multi-view multi-label (MVML) classification problem, multiple views are
simultaneously associated with multiple semantic representations. Multi-view multi-label …
simultaneously associated with multiple semantic representations. Multi-view multi-label …
An error correction prediction model based on three-way decision and ensemble learning
X Huang, J Zhan, W Ding, W Pedrycz - International Journal of Approximate …, 2022 - Elsevier
As a hot topic in machine learning, prediction has attracted a lot of attention nowadays.
Scientific prediction can provide a guide for reducing decision-making losses and making …
Scientific prediction can provide a guide for reducing decision-making losses and making …
Consistency and diversity neural network multi-view multi-label learning
In multi-view multi-label learning, each object is represented by multiple heterogeneous
data and is simultaneously associated with multiple class labels. Previous studies usually …
data and is simultaneously associated with multiple class labels. Previous studies usually …
Lung cancer subtype diagnosis using weakly-paired multi-omics data
Motivation Cancer subtype diagnosis is crucial for its precise treatment and different
subtypes need different therapies. Although the diagnosis can be greatly improved by fusing …
subtypes need different therapies. Although the diagnosis can be greatly improved by fusing …
Exploring view-specific label relationships for multi-view multi-label feature selection
In the domain of multi-view multi-label (MVML) learning, features are distributed across
various views, each offering multiple semantic representations. While existing approaches …
various views, each offering multiple semantic representations. While existing approaches …
Semisupervised graph neural networks for graph classification
Graph classification aims to predict the label associated with a graph and is an important
graph analytic task with widespread applications. Recently, graph neural networks (GNNs) …
graph analytic task with widespread applications. Recently, graph neural networks (GNNs) …
Animc: A soft approach for autoweighted noisy and incomplete multiview clustering
Multiview clustering has wide real-world applications because it can process data from
multiple sources. However, these data often contain missing instances and noises, which …
multiple sources. However, these data often contain missing instances and noises, which …
Learning view-specific labels and label-feature dependence maximization for multi-view multi-label classification
D Zhao, Q Gao, Y Lu, D Sun - Applied Soft Computing, 2022 - Elsevier
Multi-view multi-label learning tasks often appear in various critical data classification
scenarios. Each training sample has multiple heterogeneous data views associated with …
scenarios. Each training sample has multiple heterogeneous data views associated with …
Within-cross-consensus-view representation-based multi-view multi-label learning with incomplete data
This article develops a multi-view multi-label learning for incomplete data which are
ubiquitous with the usage of three kinds of representations including within-view …
ubiquitous with the usage of three kinds of representations including within-view …