Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Pairwise cross-domain factor model for heterogeneous transfer ranking
Proceedings of the fifth ACM international conference on Web search and data mining
Multiple outlooks learning with support vector machines
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Domain adaptation with topical correspondence learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a manifold alignment based approach for heterogeneous domain adaptation. A key aspect of this approach is to construct mappings to link different feature spaces in order to transfer knowledge across domains. The new approach can reuse labeled data from multiple source domains in a target domain even in the case when the input domains do not share any common features or instances. As a pre-processing step, our approach can also be combined with existing domain adaptation approaches to learn a common feature space for all input domains. This paper extends existing manifold alignment approaches by making use of labels rather than correspondences to align the manifolds. This extension significantly broadens the application scope of manifold alignment, since the correspondence relationship required by existing alignment approaches is hard to obtain in many applications.