Machine Learning - Special issue on inductive transfer
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concept decompositions for large sparse text data using clustering
Machine Learning
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Co-clustering based classification for out-of-domain documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Spectral domain-transfer learning
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A robust semi-supervised classification method for transfer learning
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
RolX: structural role extraction & mining in large graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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In machine learning problems, labeled data are often in short supply. One of the feasible solution for this problem is transfer learning. It can make use of the labeled data from other domain to discriminate those unlabeled data in the target domain. In this paper, we propose a transfer learning framework based on similarity matrix approximation to tackle such problems. Two practical algorithms are proposed, which are the label propagation and the similarity propagation. In these methods, we build a hybrid graph based on all available data. Then the information is transferred cross domains through alternatively constructing the similarity matrix for different part of the graph. Among all related methods, similarity propagation approach can make maximum use of all available similarity information across domains. This leads to more efficient transfer and better learning result. The experiment on real world text mining applications demonstrates the promise and effectiveness of our algorithms.