Transductive Learning via Spectral Graph Partitioning

By Thorsten Joachims. 2003

766 citations recorded by the source corpus.

Subfields: Graph Learning, Statistical Learning Theory

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Citations in the corpus

MixMatch: A Holistic Approach to Semi-Supervised Learning (2019, neutral): “, “transductive” models [14, 22, 21], graph-based methods [49, 4, 29], generative modeling [3, 27, 41, 9, 17, 23, 38, 34, 42], etc.”
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, background): “2004), spectral graph transducer (Joachims 2003), label propagation (Zhu, Ghahramani, and Lafferty 2003) and its variants (Zhou et al.”
A tutorial on spectral clustering (2007, methodology): “…for example spectral clustering applied to the co-clustering problem (Dhillon, 2001), spectral clustering with additional side information (Joachims, 2003) connections between spectral clustering and the weighted kernel-k -means algorithm (Dhillon, Guan, and Kulis, 2005), learning…”
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring (2019, background): “…research on “transductive” or graph-based semi-supervised learning techniques which leverage the idea that unlabeled datapoints should be assigned the label of a labeled datapoint if they are sufﬁciently similar (Gammerman et al., 1998; Joachims, 2003; 1999; Bengio et al., 2006; Liu et al., 2018).”
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples (2006, methodology): “In Table 5, we report the precision and error rates at the precision-recall break-even point averaged over 100 realizations of the data, and include results reported in Joachims (2003) for spectral graph transduction, and the cotraining algorithm (Blum and Mitchell, 1998) for comparison.”
Spectral clustering and transductive learning with multiple views (2007, background): “See also (Chapelle et al., 2003; Joachims, 2003; Zhu et al., 2003; Smola & Kondor, 2003; Belkin et al., 2004) which are closely related to (Zhou et al.”
Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective (2010, methodology): “The cross-domain learning method CoCC [3] and the semisupervised techniques TSVM [11] as well as SGT [12] can also be applied to this situation.”
A Co-Regularization Approach to Semi-supervised Learning with Multiple Views (2005, methodology): “These methods are (a) RLS, SVM on fully labeled data sets and with 12 randomly chosen labeled examples; (b) single-view semi-supervised methods: SGT (Joachims, 2003), TSVM (Joachims, 1999), Laplacian SVM, Laplacian RLS (Belkin, Niyogi & Sindhwani, 2004; Sindhwani, Niyogi & Belkin, 2005); (c) multi-v”
Semi-Supervised Graph-Based Hyperspectral Image Classification (2007, background): “The main differences between the various SSL algorithms, such as spectral methods [35], [37], [39], random walks [38],”
Group-Wise Learning for Aurora Image Classification With Multiple Representations (2019, unknown): “Spectral graphical transducer [52] provides a transductive version of the K -nearest-neighbor (KNN) classiﬁer using graph learning.”
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring (2020, background): “For example, there is a significant body of research on “transductive” or graph-based semi-supervised learning techniques which leverage the idea that unlabeled datapoints should be assigned the label of a labeled datapoint if they are sufficiently similar (Gammerman et al., 1998; Joachims, 2003; 19”