Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms

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Manifold-based Similarity Adaptation for Label Propagation (2013, background): “parameters in supervised learning [3, 4, 15], most of them need labeled instances and become unreliable under the cases with few labels.”
Efficient multiple hyperparameter learning for log-linear models (2007, methodology): “More rece ntly, similar cross-validation optimization techniques have been proposed for other supervise d learning models [3], including support vector machines [4, 10, 16], Gaussian processes [35, 33 ], and related kernel learning methods [18, 17, 39].”
Semi-supervised learning by disagreement (2010, unknown): “Zhang and Lee [79] selected a better RBF bandwidth to minimize the predictive error on labeled data using cross validation.”
Introduction to Semi-Supervised Learning (2009, unknown): “The graph construction itself is important [11, 83, 82, 29, 167, 196].”
Unifying Graph Convolutional Neural Networks and Label Propagation (2020, methodology): “…are propagated and aggregated along edges in the graph, which is known as Label Propagation Algorithm (LPA) (Zhu et al., 2005; Zhou et al., 2004; Zhang & Lee, 2007; Wang & Zhang, 2008; Karasuyama & Mamitsuka, 2013; Gong et al., 2017; Liu et al., 2019a); On the node side V , node features are…”
Multimodal Graph-Based Reranking for Web Image Search (2012, background): “The radius parameter is usually very sensitive for graph-based learning and it needs to be carefully tuned [42], [43].”