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An integrated framework for scalable and inductive graph-based semi-supervised learning with adaptive sample weighting

delete2026-07-20
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PRE
AI
F
Fadi Dornaika *
Z
Z. Ibrahim
A
A. Bosaghzadeh
DOI:10.1016/j.engappai.2026.115696delete
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摘要

摘要

En 中文
• 介绍了一种可扩展的基于图的无监督学习算法。 • 将自适应样本加权概念扩展到大型数据集。 • 将标记样本的自适应权重引入无监督模型。 • 联合提供锚标签、锚图和线性映射。 • 使用三个大型数据集评估无监督分类效果。
Keyword:
Scalable graph-based semi-supervised learning
Adaptive weights
Reduced flexible manifold embedding
Anchor-to-anchor affinity graph
Inductive semi-supervised models

期刊

Engineering Applications of Artificial Intelligence 封面图
Engineering Applications of Artificial Intelligence
IF:
8
论文数:
5.7K
被引数:
3.5W

机构

S
Shahid Rajaee Teacher Training University
学者数:
82
论文数: 58
被引数: 999
U
university of the basque country
学者数:
1.1K
论文数: 514
被引数: 0
引用论文

引用论文

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errZhang, Yan-Ming; Huang, Kaizhu; Geng, Guang-Gang; Liu, Cheng-Lin
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Weighted samples based semi-supervised classification
err2019-06-01
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PREAI
errChen, Xia; Yu, Guoxian; Tan, Qiaoyu; Wang, Jun
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Deep semi-supervised learning via dynamic anchor graph embedding in latent space
err2022-02-01
err11
errOAAI
errTu, Enmei; Wang, Zihao; Yang, Jie; Kasabov, Nikola
err分享
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