Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using KCCA for Japanese---English cross-language information retrieval and document classification
Journal of Intelligent Information Systems
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Adapting Naive Bayes to Domain Adaptation for Sentiment Analysis
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Transferring naive bayes classifiers for text classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Adaptive localization in a dynamic WiFi environment through multi-view learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Domain adaptation via transfer component analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A new approach for multi-document update summarization
Journal of Computer Science and Technology
Active learning with transfer learning
ACL '12 Proceedings of ACL 2012 Student Research Workshop
| Hi-index | 0.00 |
Transfer learning aims at leveraging the knowledge in labeled source domains to predict the unlabeled data in a target domain, where the distributions are different in domains. Among various methods for transfer learning, one kind of algorithms focus on the correspondence between bridge features and all the other specific features from different domains, and later conduct transfer learning via the single-view correspondence. However, the single-view correspondence may prevent these algorithms from further improvement due to the problem of incorrect correlation discovery. To tackle this problem, we propose a new method for transfer learning in a multi-view correspondence perspective, which is called Multi-View Principal Component Analysis (MVPCA) approach. MVPCA discovers the correspondence between bridge features representative across all domains and specific features from different domains respectively, and conducts the transfer learning by dimensionality reduction in a multi-view way, which can better depict the knowledge transfer. Experiments show that MVPCA can significantly reduce the cross domain prediction error of a baseline non-transfer method. With multiview correspondence information incorporated to the single-view transfer learning method, MVPCA can further improve the performance of one state-of-the-art single-view method.