Graph Convolutional Networks based on manifold learning for semi-supervised image classification
Due to a huge volume of information in many domains, the need for classification methods is
imperious. In spite of many advances, most of the approaches require a large amount of
labeled data, which is often not available, due to costs and difficulties of manual labeling
processes. In this scenario, unsupervised and semi-supervised approaches have been
gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a
promising solution since they encode the neighborhood information and have achieved …
imperious. In spite of many advances, most of the approaches require a large amount of
labeled data, which is often not available, due to costs and difficulties of manual labeling
processes. In this scenario, unsupervised and semi-supervised approaches have been
gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a
promising solution since they encode the neighborhood information and have achieved …
Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification
L Pascotti Valem, DC Guimarães Pedronette… - arXiv e …, 2023 - ui.adsabs.harvard.edu
Due to a huge volume of information in many domains, the need for classification methods is
imperious. In spite of many advances, most of the approaches require a large amount of
labeled data, which is often not available, due to costs and difficulties of manual labeling
processes. In this scenario, unsupervised and semi-supervised approaches have been
gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a
promising solution since they encode the neighborhood information and have achieved …
imperious. In spite of many advances, most of the approaches require a large amount of
labeled data, which is often not available, due to costs and difficulties of manual labeling
processes. In this scenario, unsupervised and semi-supervised approaches have been
gaining increasing attention. The GCNs (Graph Convolutional Neural Networks) represent a
promising solution since they encode the neighborhood information and have achieved …
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