\mathcal{L}(f)=\underbrace{\mathcal{L}_s\left(f, \mathcal{D}_l\right)}_{\text {supervised loss }}+\lambda \underbrace{\mathcal{L}_u\left(f, \mathcal{D}_u\right)}_{\text {unsupervised loss }}+\mu \underbrace{\mathcal{L}_r(f, \mathcal{D})}_{\text {regularization los...
图构造技术总结--Graph‑based semi‑supervised learning via improving the quality of the graph dynamically 前言 本博文主要对论文中提到的图构造方法进行梳理,论文自己提出的模型并未介绍,感兴趣的可以阅读原文 摘要 基于图的半监督学习GSSL主要包含两个过程:图的构建和标签推测。传统的GSSL中这两个过程是完全...
Graph-based Semi-Supervised Learning (GSSL) methods aim to classify unlabeled data by learning the graph structure and labeled data jointly. In this work, we propose a simple GSSL approach, which can deal with various degrees of class imbalance in given datasets. The key idea is to estimate...
The recent years have witnessed a surge of interests in graph-based semi-supervised learning (GBSSL). In this paper, we will introduce a series of works done by our group on this topic including: 1) a method called linear neighborhood propagation (LNP) which can automatically construct the ...
Semi-Supervised Contrastive Learning 形式上,无监督对比学习有望达到如下效果: score(f(xi),f(x+i))≫score(f(xi),f(x−i))score(f(xi),f(xi+))≫score(f(xi),f(xi−)) 也就是正样本之间的距离远远小于负样本对之间的距离,其中ff是编码器,提出的无监督对比损失如下: ...
Graph-based semi-supervised learning Recent years have witnessed a surge of interest in graph-based semi-supervised learning. However, two of the major problems in graph-based semi-supervised ... C Zhang,W Fei - 《Artificial Life & Robotics》 被引量: 0发表: 2011年 Seeing stars when there ...
Based on the theoretical analysis, we are able to demonstrate why spectral kernel design based methods can improve the predictive performance. Empirical examples are included to illustrate the main consequences of our analysis. 展开 关键词: Graph-based semi-supervised learning kernel design transductive...
[10] S. Wan, S. Pan, J. Yang, and C. Gong, “Contrastive and generative graph convolutional networks for graph-based semi-supervised learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 10049-10057.[11] M. McPherson, L. Smith-Lovin, and J. M. J....
Semi-supervised extreme learning machineLabel-consistency graphSample-similarity graphMachine learning algorithms play a critical role in electroencephalograpy (EEG)-based brain-computer interface (BCI) systems. However, collecting labeled samples for classifier training and calibration is still difficult and...
4.1 Graph-based semi-supervised learning 4.2 Neural networks on graphs 五、Experiments 5.1 Datasets 数据集的信息表如图: (1)Citation networks 本文考虑三个引文网络数据集:Citeseer、Cora和PubMed(Sen等人,2008)。数据集包含每个文档的稀疏bag-of-words特征向量和文档之间的引用链接列表。本文将引用链接视为(无...