A Co-Regularization Approach to Semi-supervised Learning with Multiple Views

By Vikas Sindhwani, Partha Niyogi, Mikhail Belkin. 2005

446 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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In Representation & Self-Supervised Learning

Citations in the corpus

Semi-Supervised Sequence Modeling with Cross-View Training (2018, background): “Particularly relevant are co-training (Blum and Mitchell, 1998) and co-regularization (Sindhwani and Belkin, 2005), which trains two models with disjoint views of the input.”
Fast Learning from Distributed Datasets without Entity Matching (2016, background): “Learning on data described by different feature sets is the t opic of multiple view learning and co-training [7, 26].”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Another co-regularized framework [98] utilizes sufficient and redundant views similar to Co-training.”
A Survey on Multi-view Learning (2013, methodology): “Blum and Mitchell (1998); Nigam and Ghani (2000); Brefeld and Scheffer (2004); Sindhwani et al. (2005); Yu et al. (2011) and Zhu et al. (2012) used the WebKB data as one of the evaluation datasets.”
Multi-view Domain Generalization for Visual Recognition (2015, methodology): “Moreover, several multi-view semi-supervised learning methods [4, 34] were developed.”
Multi-View Learning in the Presence of View Disagreement (2008, methodology): “Previous methods for overcoming insuﬃ-ciency have addressed the case where both views are necessary for classiﬁcation [5, 2, 17].”
Deep Multimodality Model for Multi-task Multi-view Learning (2019, methodology): “[22] proposed Co-regularization method to jointly regularize two Reproducing Kernel Hilbert Space H 1 and H 2 .”
Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization (2020, methodology): “From the view of agreement maximization principles [4, 34], different models would agree on labels of most examples, and they are unlikely to agree on incorrect labels.”
Recognizing RGB Images by Learning from RGB-D Data (2014, background): “Note that the semi-supervised multi-view learning methods [27] and the multi-view domain adaptation approaches [31] cannot be applied for our problem, because we only have single view of features for the samples in the target domain.”
Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (2012, methodology): “, 2009), and the multi-view coclassification method (Amini & Goutte, 2010) which is an instance of the co-regularized multi-view classification (Sindhwani et al., 2002; Sindhwani & Rosenberg, 2008).”
A Survey on Multi-View Clustering (2017, unknown): “, co-training [66], [128], co-regularization [129], [5], margin consistency [130], [131] can lend a hand to multiview clustering if there is a mechanism to estimate the initial labels.”
Efficient co-regularised least squares regression (2006, methodology): “An approach similar to non-parametric coRLSR has been proposed by (Sindhwani et al., 2005) for classification.”
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021, background): “Co-training (Blum and Mitchell, 1998) and co-regularization (Sindhwani and Niyogi, 2005) train a separate model for each view.”
Classifier learning with hidden information (2015, background): “Multi-view learning tackles the problem where features from multiple sources (or views) are available during both training and testing [15, 16, 17].”
Constrained Semi-supervised Learning in the Presence of Unanticipated Classes (2016, unknown): “[114] and Brefeld et al.”
A survey of multi-view machine learning (2013, background): “For example, the applications of multi-view learning range from dimensionality reduction [10, 20, 50] and semisupervised learning [35, 36, 38, 39, 42, 54, 56] to supervised learning [11, 16], active learning [28, 41], ensemble learning [45, 51, 55], transfer learning [12, 52, 53], and clustering [7,”