Semi-supervised learning using randomized mincuts

By Avrim Blum, John D. Lafferty, Mugizi Robert Rwebangira, Rajashekar Reddy. 2004

284 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning (2018, background): “Researches along this line include min-cuts (Blum and Chawla 2001) and randomized min-cuts (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…”
Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models (2011, unknown): “[2].”
Semi-Supervised Polarity Lexicon Induction (2009, methodology): “An improvement to the basic mincut algorithm was proposed by Blum et. al.”
Relation Extraction Using Label Propagation Based Semi-Supervised Learning (2006, background): “Recently a promising family of semi-supervised learning algorithm is introduced, which can effectively combine unlabeled data with labeled data in learning process by exploiting manifold structure (cluster structure) in data (Belkin and Niyogi, 2002; Blum and Chawla, 2001; Blum et al., 2004; Zhu and”
Label Efficient Semi-Supervised Learning via Graph Filtering (2019, background): “Another line of works rely on graph partition, where the cuts should agree with the labeled vertices and be placed in low density regions [7, 8, 28, 63], among which the most popular one is perhaps the label propagation methods [5, 15, 61].”
Sentiment Analysis and Opinion Mining (2012, methodology): “The three algorithms were Mincut ( Blum and Chawla, 2001), Randomized Mincut ( Blum et al., 2004), and label propagation ( Zhu and Ghahramani, 2002).”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, unknown): “In the experiments of Blum et al. (2004), their randomized mincut method empirically does not perform as well as a method introduced by Zhu et al. (2003).”
Semi-supervised learning by disagreement (2010, unknown): “Later, Blum et al. [12] disturbed the graph with some randomness and produced a " soft " minimum cut using majority voting.”
Introduction to Semi-Supervised Learning (2009, methodology): “Therefore, there are many related methods that exploit this idea, including Mincut [21] and randomized Mincut [20], Boltzmann machines [70, 209], graph random”
Certainty-Driven Consistency Loss for Semi-supervised Learning (2019, methodology): “Based on this assumption, many traditional algorithms have been developed, including Transductive SVM [4, 5], graph-based methods [6, 7], and noise regularization [8, 9, 10].”
Semi-supervised Learning with Deep Generative Models (2014, methodology): “It would be desirable to have a single principled loss function similar to (Blum et al., 2004) or (Zhu et al., 2003).”
Decoupled Certainty-Driven Consistency Loss for Semi-supervised Learning (2019, methodology): “ption where similar data are more likely to share the same label [1]. Based on this assumption, many traditional algorithms have been developed, including Transductive SVM [4, 5], graph-based methods [6, 7], and noise regularization [8, 9, 10]. indicate equal contributions With the tremendous succes”
Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms (2006, methodology): “(Blum et al., 2004) used the idea of bagging and perturbed the graph by adding random noise to the edge weights.”
Bootstrapped Graph Diffusions: Exposing the Power of Nonlinearity (2018, methodology): “Popular SSL methods use spectral diffusions [9] (the graph Laplacian, graph cuts [5, 6], and random walks).”
Bidirectional Helmholtz Machines (2015, background): “However, computing the probabilities in such models is expensive and it is highly dependent on the structure of the graph [9, 29].”