Kernel manifold alignment for domain adaptation

PLoS ONE

Volumn 11, Issue 2, 2016, Pages

  Tuia, Devis ^a   Camps Valls, Gustau ^b  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLASSIFICATION ALGORITHM; CORRELATION ANALYSIS; DATA ANALYSIS; DOMAIN ADAPTATION; FACIAL RECOGNITION; KERNEL METHOD; KERNEL METHOD FOR MANIFOLD ALIGNMENT; LEARNING THEORY; MACHINE LEARNING; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MATHEMATICAL PARAMETERS; NONLINEAR SYSTEM; RADEMACHER COMPLEXITY; SEMI SUPERVISED MANIFOLD ALIGNMENT; ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DATA MINING; FACIAL EXPRESSION; HUMAN; INFORMATION RETRIEVAL; PROCEDURES; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA; UTILIZATION;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; DATA MINING; FACIAL EXPRESSION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED;

EID: 84960539553     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0148655     Document Type: Article

References (47)

1. Quiñonero-Candela J, Sugiyama M, Schwaighofer A, Lawrence ND. Dataset shift in machine learning. Neural information processing series. Cambridge, Mass., London: MIT Press; 2009.
2. Saenko K, Kulis B, Fritz M, Darrell T. Adapting visual category models to new domains. In: Proc. ECCV. Berlin, Heidelberg: Springer-Verlag; 2010. p. 213-226.
3. Farhadi A, Tabrizi MK. Learning to Recognize Activities from the Wrong View Point. In: Proc. ECCV. Berlin, Heidelberg: Springer-Verlag; 2008. p. 154-166.
4. Duan L, Xu D, Tsang IW, Luo J. Visual Event Recognition in Videos by Learning from Web Data. IEEE Trans Pattern Anal Mach Intell. 2012;34(9):1667-1680. doi: 10.1109/TPAMI.2011.265 PMID: 22201057
5. Torralba A, Efros AA. Unbiased look at dataset bias. In: Proc. CVPR. Colorado Springs, CO; 2011. p. 1521-1528.
6. Pan SJ, Yang Q. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering. 2010 October; 22(10):1345-1359. doi: 10.1109/TKDE.2009.191
7. Patel VM, Gopalan R, Li R, Chellappa R. Visual Domain Adaptation: A survey of recent advances. IEEE Signal Proc Mag. 2015 May; 32(3):53-69. doi: 10.1109/MSP.2014.2347059
8. Jacobs DW, Daume H, Kumar A, Sharma A. Generalized Multiview Analysis: A discriminative latent space. In: Proc. CVPR. Providence, RH; 2012. p. 2160-2167.
9. Lai PL, Fyfe C. Kernel and Nonlinear Canonical Correlation Analysis. In: Int. J. Neural Sys.; 2000. p. 365-377. doi: 10.1142/S012906570000034X
10. Pan SJ, Yang Q. Domain adaptation via transfer component analysis. IEEE Trans Neural Networks. 2011;22:199-210. doi: 10.1109/TNN.2010.2091281 PMID: 21095864
11. Baktashmotlagh M, Harandi MT, Lovell BC, Salzmann M. Domain adaptation on the statistical manifold. In: Proc. CVPR. Columbus, OH; 2014. p. 2481-2488.
12. Gopalan R, Li R, Chellappa R. Domain adaptation for object recognition: An unsupervised approach. In: Proc. ICCV. Barcelona, Spain; 2011. p. 999-1006.
13. Gong B, Shi Y, Sha F, Grauman K. Geodesic flow kernel for unsupervised domain adaptation. In: Proc. CVPR. Providence, RH: IEEE; 2012. p. 2066-2073.
14. Gretton A, Bousquet O, Smola AJ, Schölkopf B. Measuring statistical dependence with Hilbert-Schmidt norms. In: Jain S, Lee WS, editors. Proc. Algorithmic Learn. Theory; 2005. p. 63-77.
15. Courty N, Flamary R, Tuia D. Domain adaptation with regularized optimal transport. In: Proc. ECML. Nancy, France; 2014. p. 274-289.
16. Kulis B, Saenko K, Darrell T. What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: Proc. CVPR. Colorado Springs, CO; 2011. p. 1785-1792.
17. Jhuo IH, Liu D, Lee DT, Chang SF. Robust visual domain adaptation with low-rank reconstruction. In: Proc. CVPR. Providence, RH; 2012. p. 2168-2175.
18. Hoffman J, Rodner E, Donahue J, Saenko K, Darrell T. Efficient Learning of Domain Invariant Image Representations. In: Proc. ICLR. Scottsdale, AZ; 2013.
19. Donahue J, Hoffman J, Rodner E, Saenko K, Darrell T. Semi-supervised Domain Adaptation with Instance Constraints. In: CVPR; 2013. p. 668-675.
20. Ham J, Lee D, Saul L. Semisupervised alignment of manifolds. In: Cowell RG, Ghahramani Z, editors. Proc. AISTATS. London, UK; 2005. p. 120-127.
21. Wang C, Krafft P, Mahadevan S. Manifold alignment. In: Ma Y, Fu Y, editors. Manifold Learning: Theory and Applications. CRC Press; 2011.
22. Hotelling H. Relations Between Two Sets of Variates. Biometrika. 1936 Dec; 28 (3/4):321-377. doi: 10. 1093/biomet/28.3-4.321
23. Wang C, Mahadevan S. Heterogeneous domain adaptation using manifold alignment. In: IJCAI. Barcelona, Spain; 2011. p. 1541-1546.
24. Jolliffe IT. Principal Component Analysis. New York: Springer; 1986.
25. Schölkopf B, Smola AJ, Müller KR. Nonlinear component analysis as a kernel Eigenvalue problem. Neural Comput. 1998;10:1299-1319. doi: 10.1162/089976698300017467
26. Long M, Wang J, Ding G, Sun J, Yu PS. Transfer Feature Learning with Joint Distribution Adaptation. In: ICCV; 2013. p. 2200-2207.
27. Tuia D, Muñoz-Marí J, Gómez-Chova L, Malo J. Graph matching for adaptation in remote sensing. IEEE Trans Geosci Remote Sens. 2013;51(1):329-341. doi: 10.1109/TGRS.2012.2200045
28. Mika S, Schölkopf B, Smola A, Müller KR, Scholz M, Rätsch G. Kernel PCA and De-Noising in Feature Spaces. In: NIPS 11. MIT Press; 1999. p. 536-542.
29. Bakir G, Weston J, Schölkopf B. Learning to find Pre-images. In: Proc. NIPS; 2003.
30. Kwok JT, Tsang IW. The Pre-Image Problem in Kernel Methods. IEEE Trans Neural Networks. 2004;15(6):1517-1525. doi: 10.1109/TNN.2004.837781 PMID: 15565778
31. Chapelle O, Schölkopf B, Zien A. Semi-Supervised Learning. 1st ed. Cambridge, MA and London, England: MIT Press; 2006.
32. Camps-Valls G, Bandos T, Zhou D. Semi-supervised Graph-based Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 45(10):2044-3054.
33. Tuia D, Volpi M, Trolliet M, Camps-Valls G. Semisupervised Manifold Alignment of Multimodal Remote Sensing Images. IEEE Trans Geosci Remote Sens. 2014;52(12):7708-7720. doi: 10.1109/TGRS. 2014.2317499
34. Tuia D, Trolliet M, Volpi M. Multisource alignment of image manifolds. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS. Melbourne, Australia; 2013.
35. Reed M, Simon B. I: Functional Analysis, Volume 1 (Methods of Modern Mathematical Physics) (vol 1). 1st ed. Academic Press; 1981.
36. Riesz F, Nagy BS. Functional Analysis. Frederick Ungar Publishing Co.; 1955.
37. Yan S, Xu D, Zhang B, Zhang HJ, Yang Q, Lin S. Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. IEEE Trans Patt Anal Mach Intell. 2007;29(1):40-51. doi: 10.1109/TPAMI.2007.250598
38. Zhu F, P H, Kallas M. Kernel nonnegative matrix factorization without the pre-image problem. In: Machine Learning for Signal Processing. Reims, France; 2014.
39. Rahimi A, Recht B. Random Features for Large-Scale Kernel Machines. In: Neural Information Processing Systems; 2007.
40. Jones LK. Annals of Statistics. A simple lemma on greedy approximation in Hilbert space and convergence rates for projection pursuit regression and neural network training. 1992;20:608-613.
41. Shawe-Taylor J, Williams CKI, Cristianini N, Kandola J. On the eigenspectrum of the Gram matrix and the generalization error of kernel-PCA. IEEE Trans Info Theory. 2005;51(7):2510-2522. doi: 10.1109/TIT.2005.850052
42. Dhanjal C, Gunn SR, Shawe-Taylor J. Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares. IEEE Trans Pattern Anal Mach Intell. 2009;31(8):1347-1361. doi: 10.1109/TPAMI. 2008.171 PMID: 19542571
43. Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, et al. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. In: Proceedings of The 31st International Conference on Machine Learning; 2014. p. 647-655.
44. Maji S, Berg AC, Malik J. Classification using intersection kernel support vector machines is efficient. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008), 24-26 June 2008, Anchorage, Alaska, USA; 2008.
45. Song L, Smola A, Gretton A, Borgwardt KM. A Dependence Maximization View of Clustering. In: Proc. ICML. Corvallis, OR; 2007. p. 815-822.
46. Mackey L, Jordan MI, Chen RY, Farrell B, Tropp JA. Matrix Concentration Inequalities via the Method of Exchangeable Pairs. Annals of Probability. 2014;.
47. Lopez-Paz D, Sra S, Smola AJ, Ghahramani Z, Schölkopf B. Randomized Nonlinear Component Analysis. In: Proceedings of the 31 st International Conference on Machine Learning. Beijing, China; 2014. p. 1-9.