Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Knowledge transfer via multiple model local structure mapping
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting discriminative concepts for domain adaptation in text mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Multi-label boosting for image annotation by structural grouping sparsity
Proceedings of the international conference on Multimedia
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-view transfer learning with a large margin approach
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Support Vector Machines Trained in the Primal
The Journal of Machine Learning Research
Geodesic flow kernel for unsupervised domain adaptation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Domain adaptation for object recognition: An unsupervised approach
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
IEEE Transactions on Knowledge and Data Engineering
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In many data mining applications, we often face the problem of cross-domain learning, i.e., to transfer the already learned knowledge from a source domain to a target domain. In particular, this problem becomes very challenging when there is no or little labeled training data available in the target domain, which is not an uncommon scenario as it is expensive and in certain cases even impossible to obtain any labeled training data in the target domain in many real world applications. In the literature, though few efforts are reported to attempt to solve this challenging problem, the solutions are all rather limited making this problem still open and challenging. On the other hand, as it is not uncommon to face this problem in many applications, an effective solution to this problem shall generate substantial societal impacts. In this paper, we address this problem and propose a new framework, called DISMUTE, taking advantage of the typically available multiple views of the data in domains. Consequently, DISMUTE is based on discriminative feature selection for multi-view cross-domain learning. Theoretic analysis and extensive evaluations in the specific application of object identification and image classification against several state-of-the-art methods demonstrate the outstanding superiority of DISMUTE.