Advertisement

Simultaneous Nonlinear Label-Instance Embedding for Multi-label Classification

  • Keigo KimuraEmail author
  • Mineichi Kudo
  • Lu Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

In this paper, unlike previous many linear embedding methods, we propose a non-linear embedding method for multi-label classification. The algorithm embeds both instances and labels into the same space, reflecting label-instance relationship, label-label relationship and instance-instance relationship as faithfully as possible, simultaneously. Such an embedding into two-dimensional space is useful for simultaneous visualization of instances and labels. In addition linear and nonlinear mapping methods of a testing instance are also proposed for multi-label classification. The experiments on thirteen benchmark datasets showed that the proposed algorithm can deal with better small-scale problems, especially in the number of instances, compared with the state-of-the-art algorithms.

Keywords

Multi-label classification Nonlinear embedding Visualization 

Notes

Acknowledgment

We would like to thank Dr. Kush Bhatia for providing the code of SLEEC and large-scale datasets. This work was partially supported by JSPS KAKENHI Grant Number 14J01495 and 15H02719.

References

  1. 1.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bengio, Y., Paiement, J.F., Vincent, P., Delalleau, O., Le Roux, N., Ouimet, M.: Out-of-sample extensions for LLE, Isomap, MDS, Eigenmaps, and spectral clustering. Adv. Neural Inf. Process. Syst. 16, 177–184 (2004)Google Scholar
  3. 3.
    Bhatia, K., Jain, H., Kar, P., Varma, M., Jain, P.: Sparse local embeddings for extreme multi-label classification. Adv. Neural Inf. Process. Syst. 28, 730–738 (2015)Google Scholar
  4. 4.
    Chen, Y.N., Lin, H.T.: Feature-aware label space dimension reduction for multi-label classification. In: Advances in Neural Information Processing Systems, pp. 1529–1537 (2012)Google Scholar
  5. 5.
    Hsu, D., Kakade, S., Langford, J., Zhang, T.: Multi-label prediction via compressed sensing. Adv. Neural Inf. Process. Syst. 22, 772–780 (2009)Google Scholar
  6. 6.
    Lin, Z., Ding, G., Hu, M., Wang, J.: Multi-label classification via feature-aware implicit label space encoding. In: Proceedings of the 31st International Conference on Machine Learning, pp. 325–333 (2014)Google Scholar
  7. 7.
    Liu, T., Moore, A.W., Yang, K., Gray, A.G.: An investigation of practical approximate nearest neighbor algorithms. In: Advances in Neural Information Processing Systems, pp. 825–832 (2004)Google Scholar
  8. 8.
    Tai, F., Lin, H.T.: Multilabel classification with principal label space transformation. Neural Comput. 24(9), 2508–2542 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Talwalkar, A., Kumar, S., Rowley, H.: Large-scale manifold learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2008)Google Scholar
  10. 10.
    Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Department of Informatics, Aristotle University of Thessaloniki, Greece (2006)Google Scholar
  11. 11.
    Tsoumakas, G., Spyromitros-Xioufis, E., Vilcek, J., Vlahavas, I.: Mulan: a Java library for multi-label learning. J. Mach. Learn. Res. 12, 2411–2414 (2011)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Weston, J., Bengio, S., Usunier, N.: Wsabie: scaling up to large vocabulary image annotation. IJCAI 11, 2764–2770 (2011)Google Scholar
  13. 13.
    Yu, H.f., Jain, P., Kar, P., Dhillon, I.: Large-scale multi-label learning with missing labels. In: Proceedings of the 31st International Conference on Machine Learning, pp. 593–601 (2014)Google Scholar
  14. 14.
    Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)CrossRefzbMATHGoogle Scholar
  15. 15.
    Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Schneider, J.G.: Multi-label output codes using canonical correlation analysis. In: International Conference on Artificial Intelligence and Statistics, pp. 873–882 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan

Personalised recommendations