Unsupervised domain adaptation using eigenanalysis in kernel space for categorisation tasks

Unsupervised domain adaptation using eigenanalysis in kernel space for categorisation tasks

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study describes a new technique of unsupervised domain adaptation based on eigenanalysis in kernel space, for the purpose of categorisation tasks. The authors propose a transformation of data in source domain, such that the eigenvectors and eigenvalues of the transformed source domain become similar to that of the target domain. They extend this idea to the reproducing kernel Hilbert space, which enables to deal with non-linear transformation of source domain. They also propose a measure to obtain the appropriate number of eigenvectors needed for transformation. Results on object, video and text categorisations tasks using real-world datasets show that the proposed method produces better results when compared with a few recent state-of-art methods of domain adaptation.


    1. 1)
    2. 2)
      • 2. Beijbom, O.: ‘Domain adaptation for computer vision applications’. Technical report, University of California, San Diego, June 2012.
    3. 3)
    4. 4)
      • 4. Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., Schölkopf, B.: ‘Covariate shift by kernel mean matching’, in Quiñonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N., (Eds.): ‘Dataset shift in machine learning’, (The MIT Press, 2009), Ch. 8, pp. 131160.
    5. 5)
      • 5. Howard, A., Jebara, T.: ‘Transformation learning via kernel alignment’. Int. Conf. on Machine Learning and Applications, 2009, pp. 301308.
    6. 6)
      • 6. Gopalan, R., Li, R., Chellappa, R.: ‘Domain adaptation for object recognition: an unsupervised approach’. IEEE Int. Conf. on Computer Vision, 2011, pp. 9991006.
    7. 7)
      • 7. Gong, B., Shi, Y., Sha, F., Grauman, K.: ‘Geodesic flow Kernel for unsupervised domain adaptation’. IEEE Conf. on Computer Vision and Pattern Recognition, 2012, pp. 20662073.
    8. 8)
      • 8. Samanta, S., Das, S.: ‘Modeling sequential domain shift through estimation of subspaces for categorization’. British Machine Vision Conf., 2014.
    9. 9)
      • 9. Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: ‘Unsupervised visual domain adaptation using subspace alignment’. IEEE Int. Conf. in Computer Vision, 2013.
    10. 10)
      • 10. Saenko, K., Kulis, B., Fritz, M., Darrell, T.: ‘Adapting visual category models to new domains’. European Conf. on Computer Vision, 2010, pp. 213226.
    11. 11)
      • 11. Samanta, S., Das, S.: ‘Domain adaptation based on eigen-analysis and clustering, for object categorization’. Int. Conf. on Computer Analysis of Images and Patterns, 2013, pp. 245253.
    12. 12)
      • 12. Hamm, J., Lee, D.D.: ‘Grassmann discriminant analysis: a unifying view on subspace-based learning’. Int. Conf. on Machine Learning, 2008, pp. 376383.
    13. 13)
      • 13. Shawe-Taylor, J., Cristianini, N.: ‘Kernel methods for pattern analysis’ (Cambridge University Press, New York, NY, USA, 2004).
    14. 14)
      • 14. Shawe-Taylor, J., Williams, C.K.I.: ‘The stability of kernel principal components analysis and its relation to the process eigenspectrum’. In: Becker, S., Thrun, S., Obermayer, K., (Eds.): ‘Advances in neural information processing systems 15 Neural Information Processing Systems’, (NIPS, MIT Press, Vancouver, British Columbia, Canada, 9-14 December 2002), pp. 367374.
    15. 15)
    16. 16)
    17. 17)
      • 17. Duan, L., Xu, D., Chang, S.-F.: ‘Exploiting web images for event recognition in consumer videos: a multiple source domain adaptation approach’. IEEE Conf. on Computer Vision and Pattern Recognition, 2012, pp. 13381345.
    18. 18)
      • 18. Dai, W., Yang, Q., Xue, G.-R., Yu, Y.: ‘Boosting for transfer learning’. Int. Conf. on Machine Learning, 2007, pp. 193200.
    19. 19)
      • 19. Li, L., Jin, X., Long, M.: ‘Topic correlation analysis for cross-domain text classification’. AAAI Conf. on Artificial Intelligence, 2012, pp. 17.
    20. 20)
      • 20. Pan, S.J., Ni, X., Sun, J.-T., Yang, Q., Chen, Z.: ‘Cross-domain sentiment classification via spectral feature alignment’. Int. Conf. on World Wide Web, 2010, pp. 751760.

Related content

This is a required field
Please enter a valid email address