Domain adaptation via transfer component analysis

Authors:
Sinno Jialin Pan;Ivor W. Tsang;James T. Kwok;Qiang Yang
Affiliations:
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;School of Computer Engineering, Nanyang Technological University, Singapore;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
Venue:
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Year:
2009

Citing 10
Cited 24

Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
IMPLICITLY RESTARTED ARNOLDI/LANCZOS METHODS FOR LARGE SCALE EIGENVALUE CALCULATIONS

IMPLICITLY RESTARTED ARNOLDI/LANCZOS METHODS FOR LARGE SCALE EIGENVALUE CALCULATIONS
On the influence of the kernel on the consistency of support vector machines

The Journal of Machine Learning Research
Integrating structured biological data by Kernel Maximum Mean Discrepancy

Bioinformatics
A high-performance semi-supervised learning method for text chunking

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Estimating Location Using Wi-Fi

IEEE Intelligent Systems
Multi-task learning for HIV therapy screening

Proceedings of the 25th international conference on Machine learning
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

Learning to rank only using training data from related domain

Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Transfer metric learning by learning task relationships

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A robust semi-supervised classification method for transfer learning

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Hybrid active learning for cross-domain video concept detection

Proceedings of the international conference on Multimedia
Opinion mining by transformation-based domain adaptation

TSD'10 Proceedings of the 13th international conference on Text, speech and dialogue
Predictive distribution matching SVM for multi-domain learning

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Semi-supervised projection clustering with transferred centroid regularization

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Unsupervised selective transfer learning for object recognition

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Transfer learning via multi-view principal component analysis

Journal of Computer Science and Technology - Special issue on natural language processing
Domain adaptation for text categorization by feature labeling

ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Query weighting for ranking model adaptation

HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Multi-source domain adaptation and its application to early detection of fatigue

Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive boosting for transfer learning using dynamic updates

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Feature selection for transfer learning

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
On the usefulness of similarity based projection spaces for transfer learning

SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Transfer Metric Learning with Semi-Supervised Extension

ACM Transactions on Intelligent Systems and Technology (TIST)
Transfer learning with local smoothness regularizer

APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Bi-weighting domain adaptation for cross-language text classification

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Batch mode active sampling based on marginal probability distribution matching

Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Multisource domain adaptation and its application to early detection of fatigue

ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Linear semi-supervised projection clustering by transferred centroid regularization

Journal of Intelligent Information Systems
Cross-domain sparse coding

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Batch Mode Active Sampling Based on Marginal Probability Distribution Matching

ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Instance selection and instance weighting for cross-domain sentiment classification via PU learning

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a Reproducing Kernel Hilbert Space (RKHS) using Maximum Mean Discrepancy (MMD). In the subspace spanned by these transfer components, data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. The main contribution of our work is that we propose a novel feature representation in which to perform domain adaptation via a new parametric kernel using feature extraction methods, which can dramatically minimize the distance between domain distributions by projecting data onto the learned transfer components. Furthermore, our approach can handle large datsets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach in are verified by experiments on two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.