Semi-supervised support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Evaluating a probabilistic model for cross-lingual information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Annotating photo collections by label propagation according to multiple similarity cues
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Transductive multi-label learning for video concept detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Graph-based semi-supervised learning with multiple labels
Journal of Visual Communication and Image Representation
Semi-supervised multi-label learning by constrained non-negative matrix factorization
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Soft-supervised learning for text classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Robust label propagation on multiple networks
IEEE Transactions on Neural Networks
Local-driven semi-supervised learning with multi-label
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Recommendations Over Domain Specific User Graphs
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Translingual document representations from discriminative projections
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Mr.KNN: soft relevance for multi-label classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
SemiCCA: Efficient Semi-supervised Learning of Canonical Correlations
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Improving Classifier Performance Using Data with Different Taxonomies
IEEE Transactions on Knowledge and Data Engineering
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We present a machine learning task, which we call bidirectional semi-supervised learning, where label-only samples are given as well as labeled and unlabeled samples. A label-only sample contains the label information of the sample but not the feature information. Then, we propose a simple and effective graph-based method for bidirectional semi-supervised learning in multi-label classification. The proposed method assumes that correlated classes are likely to have the same labels among the similar samples. First, we construct a graph that represents similarities between samples using labeled and unlabeled samples in the same way with graph-based semi-supervised methods. Second, we construct another graph using labeled and label-only samples by connecting classes that are likely to co-occur, which represents correlations between classes. Then, we estimate labels of unlabeled samples by propagating labels over these two graphs. We can find a closed-form global solution for the label propagation by using matrix algebra. We demonstrate the effectiveness of the proposed method over supervised and semi-supervised learning methods with experiments using synthetic and multi-label text data sets.