Domain generalization via invariant feature representation

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 1, 2013, Pages 10-18

  Muandet, Krikamol ^a   Balduzzi, David ^b   Schölkopf, Bernhard ^a  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

CLASSIFIER PERFORMANCE; COMPONENT ANALYSIS; FUNCTIONAL RELATIONSHIP; GENERALIZATION ABILITY; INVARIANT FEATURES; INVARIANT TRANSFORMATIONS; OPTIMIZATION ALGORITHMS; REAL-WORLD DATASETS;

LEARNING ALGORITHMS;

EID: 84897517066     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Multi-task feature learning. In Advances in Neural Information Processing Systems 19, pages 41-48. MIT Press, 2007.
2. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Advances in Neural Information Processing Systems 19, pages 137-144. MIT Press, 2007.
3. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan. A theory of learning from different domains. Machine Learning, 79:151-175, 2010.
4. A. Berlinet and T. C. Agnan. Reproducing kernel Hilbert spaces in probability and statistics. Kluwer Academic Publishers, 2004.
5. S. Bickel, M. Brückner, and T. Scheffer. Discriminative learning under covariate shift. Journal of Machine Learning Research, pages 2137-2155, 2009.
6. G. Blanchard, G. Lee, and C. Scott. Generalizing from several related classification tasks to a new unlabeled sample. In Advances in Neural Information Processing Systems 24, pages 2178-2186, 2011.
7. R. R. Brinkman, M. Gasparetto, S.-J. J. Lee, A. J. Ribickas, J. Perkins, W. Janssen, R. Smiley, and C. Smith. High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease. Biol Blood Marrow Transplant, 13(6):691-700, 2007. ISSN 1083-8791.
8. A. Christmann and I. Steinwart. Universal kernels on Non-Standard input spaces. In Advances in Neural Information Processing Systems 23, pages 406-414. MIT Press, 2010.
9. K. Fukumizu, F. R. Bach, and M. I. Jordan. Kernel Dimensionality Reduction for Supervised Learning. In Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.
10. A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Scholkopf. Dataset Shift in Machine Learning, chapter Covariate Shift by Kernel Mean Matching, pages 131-160. MIT Press, 2009.
11. Q. Gu and J. Zhou. Learning the shared subspace for multi-task clustering and transductive transfer classification. In Proceedings of the 9th IEEE International Conference on Data Mining, pages 159-168. IEEE Computer Society, 2009.
12. M. Kim and V. Pavlovic. Central subspace dimensionality reduction using covariance operators. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4):657-670, 2011.
13. K.-C. Li. Sliced inverse regression for dimension reduction. Journal of the American Statistical Association, 86(414):316-327, 1991.
14. K. Muandet, K. Fukumizu, F. Dinuzzo, and B. Schölkopf. Learning from distributions via support measure machines. In Advances in Neural Information Processing Systems 25, pages 10-18. MIT Press, 2012.
15. S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345-1359, October 2010.
16. S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2):199-210, 2011.
17. A. Passos, P. Rai, J. Wainer, and H. D. III. Flexible modeling of latent task structures in multitask learning. In Proceedings of the 29th international conference on Machine learning, Edinburgh, UK, 2012.
18. J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence. Dataset Shift in Machine Learning. MIT Press, 2009.
19. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5):1299-1319, July 1998.
20. A. Smola, A. Gretton, L. Song, and B. Schölkopf. A Hilbert space embedding for distributions. In Proceedings of the 18th International Conference In Algorithmic Learning Theory, pages 13-31. Springer-Verlag, 2007.
21. B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. R. G. Lanckriet. Hilbert space embeddings and metrics on probability measures. Journal of Machine Learning Research, 99: 1517-1561, 2010.
22. G. Widmer and M. Kurat. Learning in the Presence of Concept Drift and Hidden Contexts. Machine Learning, 23:69-101, 1996. (Pubitemid 126737384)
23. H.-M. Wu. Kernel sliced inverse regression with applications to classification. Journal of Computational and Graphical Statistics, 17(3):590-610, 2008.