Label Propagation and Quadratic Criterion

By Olivier Chapelle, Bernhard Schölkopf, Alexander Zien. 2006

302 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Yoshua Bengio
2. Charu C. Aggarwal
3. Dacheng Tao
4. Sergey Levine
5. Percy Liang
6. Ruslan Salakhutdinov
7. Trevor Darrell
8. Quoc V. Le

Citations in the corpus

• Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, background): “…(Blum et al. 2004), spectral graph transducer (Joachims 2003), label propagation (Zhu, Ghahra-mani, and Lafferty 2003) and its variants (Zhou et al. 2004; Bengio, Delalleau, and Le Roux 2006), modified adsorption (Talukdar and Crammer 2009), and iterative classification algorithm (Sen et al. 2008).”
• Simple, robust, scalable semi-supervised learning via expectation regularization (2007, methodology): “In this paper we compare against a representative graph-based label propagation method called Quadratic Cost Criterion (QC) (Bengio et al., 2006) whose results are reported in Chapelle et al. (2006).”
• SemiBoost: Boosting for Semi-Supervised Learning (2009, methodology): “Label Propagation [3], [ 4 ] Graph-based; Maximize label consistency using Graph Laplacian T”
• Social Network Data Analytics (2011, methodology): “[4] relates the semi-supervised learning methods in the context of optimizing a quadratic cost function.”
• Learning Algorithms for the Classification Restricted Boltzmann Machine (2012, background): “Semi-supervised learning algorithms (Chapelle et al., 2006) address this situation by leveraging the unlabeled data to bias learning towards solutions that are also“con istent” with the unlabeled data.”
• Semantic Curiosity for Active Visual Learning (2020, unknown): “Labels have been propagated in videos to simplify annotations [51], improve prediction performance given limited data [4, 6], as well as collect images [15].”
• Federated Semi-Supervised Learning with Inter-Client Consistency (2020, background): “While there exist numerous work on SSL, such as transductive models [18], graph-based approaches [19], and generative modeling [20], we mainly discuss consistency regularization and entropy-minimization approaches.”
• Semi-Supervised Learning via Generalized Maximum Entropy (2010, methodology): “In Table 2, we report transduction error on Digit 1, USPS 2 and COIL data sets from (Chapelle et al., 2006) for Logistic Regression (LR) and Kernel Logistic Regression (KLR) both augmented with pairwise (PW) and expectation (EXP) penalties.”
• A Survey on Deep Semi-Supervised Learning (2021, background): “data, it is possible to learn with very few labels to propagate information through the graph [18], [19], [20], [21], [34], [35].”
• Randomized Iterative Methods for Linear Systems (2015, background): “In machine learning, applications arise for the problem of finding optimal configurations in Gaussian Markov random fields [32], in graph-based semisupervised learning and other graph-Laplacian problems [2], in least-squares SVMs, in Gaussian processes, and in others.”