Learning from a mixture of labeled and unlabeled examples with parametric side information
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IEEE Transactions on Information Theory - Part 2
MISSL: multiple-instance semi-supervised learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
SmartLabel: an object labeling tool using iterated harmonic energy minimization
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Neighbor search with global geometry: a minimax message passing algorithm
Proceedings of the 24th international conference on Machine learning
Simple, robust, scalable semi-supervised learning via expectation regularization
Proceedings of the 24th international conference on Machine learning
Image-Based Modeling by Joint Segmentation
International Journal of Computer Vision
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
Probabilistic dyadic data analysis with local and global consistency
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Prototype vector machine for large scale semi-supervised learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Graph-based semi-supervised learning as a generative model
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Some new directions in graph-based semi-supervised learning
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Scaling up semi-supervised learning: an efficient and effective LLGC variant
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mixture model label propagation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Semi-supervised classification by local coordination
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Pick your neighborhood: improving labels and neighborhood structure for label propagation
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
Multiple-Instance learning via random walk
ECML'06 Proceedings of the 17th European conference on Machine Learning
PatentMiner: topic-driven patent analysis and mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A new relational Tri-training system with adaptive data editing for inductive logic programming
Knowledge-Based Systems
Mining competitive relationships by learning across heterogeneous networks
Proceedings of the 21st ACM international conference on Information and knowledge management
Unsupervised non-parametric kernel learning algorithm
Knowledge-Based Systems
A multi-manifold semi-supervised Gaussian mixture model for pattern classification
Pattern Recognition Letters
Proceedings of the Fourth Symposium on Information and Communication Technology
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Graph-based methods for semi-supervised learning have recently been shown to be promising for combining labeled and unlabeled data in classification problems. However, inference for graph-based methods often does not scale well to very large data sets, since it requires inversion of a large matrix or solution of a large linear program. Moreover, such approaches are inherently transductive, giving predictions for only those points in the unlabeled set, and not for an arbitrary test point. In this paper a new approach is presented that preserves the strengths of graph-based semi-supervised learning while overcoming the limitations of scalability and non-inductive inference, through a combination of generative mixture models and discriminative regularization using the graph Laplacian. Experimental results show that this approach preserves the accuracy of purely graph-based transductive methods when the data has "manifold structure," and at the same time achieves inductive learning with significantly reduced computational cost.