Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Applying co-training methods to statistical parsing
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
A hybrid generative/discriminative approach to semi-supervised classifier design
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
KCK-Means: A Clustering Method Based on Kernel Canonical Correlation Analysis
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
Improving Transductive Support Vector Machine by Ensembling
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Semi-supervised document retrieval
Information Processing and Management: an International Journal
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
New Labeling Strategy for Semi-supervised Document Categorization
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Experimental comparison of semi-supervised learning method based on kernels strategy
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Multiple-view multiple-learner active learning
Pattern Recognition
Semi-supervised learning based on nearest neighbor rule and cut edges
Knowledge-Based Systems
Combining Local and Global KNN With Cotraining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Active learning with extremely sparse labeled examples
Neurocomputing
A novel initialization method for semi-supervised clustering
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Face recognition using consistency method and its variants
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Transfer learning via multi-view principal component analysis
Journal of Computer Science and Technology - Special issue on natural language processing
Discriminative deep belief networks for visual data classification
Pattern Recognition
Fuzzy semi-supervised support vector machines
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Can irrelevant data help semi-supervised learning, why and how?
Proceedings of the 20th ACM international conference on Information and knowledge management
Multiple-View Multiple-Learner Semi-Supervised Learning
Neural Processing Letters
A multi-view regularization method for semi-supervised learning
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Unlabeled data and multiple views
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
WSABIE: scaling up to large vocabulary image annotation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Analysis of co-training algorithm with very small training sets
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Exploiting unlabeled data to enhance ensemble diversity
Data Mining and Knowledge Discovery
Learning with weak views based on dependence maximization dimensionality reduction
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Document Re-ranking Using Partial Social Tagging
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Multi-view embedding learning for incompletely labeled data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Analysis of unsupervised template update in biometric recognition systems
Pattern Recognition Letters
Learning semantic representations of objects and their parts
Machine Learning
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In semi-supervised learning, a number of labeled examples are usually required for training an initial weakly useful predictor which is in turn used for exploiting the unlabeled examples. However, in many real-world applications there may exist very few labeled training examples, which makes the weakly useful predictor difficult to generate, and therefore these semisupervised learning methods cannot be applied. This paper proposes a method working under a two-view setting. By taking advantages of the correlations between the views using canonical component analysis, the proposed method can perform semi-supervised learning with only one labeled training example. Experiments and an application to content-based image retrieval validate the effectiveness of the proposed method.