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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 …
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 …
Double-layer hybrid-label identification feature selection for multi-view multi-label learning
Multi-view multi-label feature selection aims to select informative features where the data are
collected from multiple sources with multiple interdependent class labels. For fully exploiting …
collected from multiple sources with multiple interdependent class labels. For fully exploiting …
Align while fusion: A generalized nonaligned multiview multilabel classification method
Q Zhong, G Lyu, Z Yang - IEEE Transactions on Neural …, 2024 - ieeexplore.ieee.org
In the task of multiview multilabel (MVML) classification, each object is described by several
heterogeneous view features and annotated with multiple relevant labels. Existing MVML …
heterogeneous view features and annotated with multiple relevant labels. Existing MVML …
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) …
L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification
In the task of multiview multilabel (MVML) classification, each instance is represented by
several heterogeneous features and associated with multiple semantic labels. Existing …
several heterogeneous features and associated with multiple semantic labels. Existing …
Beyond shared subspace: A view-specific fusion for multi-view multi-label learning
In multi-view multi-label learning (MVML), each instance is described by several
heterogeneous feature representations and associated with multiple valid labels …
heterogeneous feature representations and associated with multiple valid labels …
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 …
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 …
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 …