Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Convex Optimization
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting for transfer learning
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
Bioinformatics
Topic-bridged PLSA for cross-domain text classification
Proceedings of the 31st annual 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
Transfer learning via dimensionality reduction
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Statistical Analysis and Data Mining
Transferring topical knowledge from auxiliary long texts for short text clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Heterogeneous domain adaptation using manifold alignment
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Domain Adaptation via Transfer Component Analysis
IEEE Transactions on Neural Networks
Regularized nonnegative shared subspace learning
Data Mining and Knowledge Discovery
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A serious and ubiquitous issue in machine learning is the lack of sufficient training data in a domain of interest. Domain adaptation is an effective approach to dealing with this problem by transferring information or models learned from related, albeit distinct, domains to the target domain. We develop a novel domain adaptation method for text document classification under the framework of Non-negative Matrix Factorization. Two key ideas of our method are to construct a latent topic space where a topic is decomposed into common words shared by all domains and words specific to individual domains, and then to establish associations between words in different domains through the common words as a bridge for knowledge transfer. The correspondence between cross-domain topics leads to more coherent distributions of source and target domains in the new representation while preserving the predictive power. Our new method outperformed several state-of-the-art domain adaptation methods on several benchmark datasets.