Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Subclassifiers in Text Categorization: A DST-Based Solution and a Case Study
IEEE Transactions on Knowledge and Data Engineering
Large margin transductive transfer learning
Proceedings of the 18th ACM conference on Information and knowledge management
Learning to rank only using training data from related domain
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Multi-view transfer learning with a large margin approach
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain Adaptation via Transfer Component Analysis
IEEE Transactions on Neural Networks
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We study to incorporate multiple views of data in a perceptive transfer learning framework and propose a Multi-view Discriminant Transfer (MDT) learning approach for domain adaptation. The main idea is to find the optimal discriminant weight vectors for each view such that the correlation between the two-view projected data is maximized, while both the domain discrepancy and the view disagreement are minimized simultaneously. Furthermore, we analyze MDT theoretically from discriminant analysis perspective to explain the condition and reason, under which the proposed method is not applicable. The analytical results allow us to investigate whether there exist within-view and/or between-view conflicts, and thus provides a deep insight into whether the transfer learning algorithm work properly or not in the view-based problems and the combined learning problem. Experiments show that MDT significantly outperforms the state-of-the-art baselines including some typical multi-view learning approaches in single-or cross-domain.