Skip to main content
Springer Nature Link
Log in

DKE-RLS: A Manifold Reconstruction Algorithm in Label Spaces with Double Kernel Embedding-Regularized Least Square

  • Conference paper
  • First Online:
PRICAI 2018: Trends in Artificial Intelligence (PRICAI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11012))

Included in the following conference series:

Abstract

Dimension reduction is an important preprocess for multi-label classification, such as feature extraction. This paper attempts to explore multi-label learning in the label space. Our approach works using machine learning’s smoothness assumption, where nearby points are more likely to share the same label and the feature manifold and label manifold can share the local topology structure. Thus, here we propose a new multi-label feature-extraction algorithm with a new method for embedding regression, i.e., manifold regularization learning in the subspace formed by multi-labels to reconstruct and use the label manifold. We integrate two least-squares formulas by linear combination, and establish the regression estimation for multi-label manifold learning. To test our approach, we conduct multiple experiments and compare our algorithm against four other multi-label learning algorithms. Results show that our approach significantly improves the performance of label manifold learning.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Rubin, T.N., Chambers, A., Smyth, P., Steyvers, M.: Statistical topic models for multi-label document classification. Mach. Learn. 88(1–2), 157–208 (2012)

    Article  MathSciNet  Google Scholar 

  2. Yang, B., Sun, J., Wang, T., Chen, Z.: Effective multilabel active learning for text classification. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 917–926 (2009)

    Google Scholar 

  3. Cabral, R.S., Torre, F., Costeira, J.P., Bernardino, A.: Matrix completion for multi-label image classification. In: Advances in Neural Information Processing Systems, pp. 190–198 (2011)

    Google Scholar 

  4. Wang, H., Huang, H., Ding, C.: Image annotation using multi-label correlated green’s function. In: 12th International Conference on Computer Vision, pp. 2029–2034 (2009)

    Google Scholar 

  5. Lo, H., Wang, J., Wang, H., Lin, S.: Costsensitive multi-label learning for audio tag annotation and retrieval. IEEE Trans. Multimedia 13(3), 518–529 (2011)

    Article  Google Scholar 

  6. Sanden, C., Zhang, J.: Enhancing multi-label music genre classification through ensemble techniques. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 705–714 (2011)

    Google Scholar 

  7. Geng, X., Ji, R.: Label distribution learning. In: Proceedings of the International Conference on Data Mining Workshops, Dallas, TA, pp. 377–383 (2013)

    Google Scholar 

  8. Zhu, X., Lafferty, J., Rosenfeld, R.: Semi-supervised learning with graphs. In: International Joint Conference on Natural Language Processing, vol. 6493, no. 10, pp. 2465–2472 (2005)

    Google Scholar 

  9. Hou, P., Geng, X., Zhang, M.: Multi-label manifold learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp. 1680–1686 (2016)

    Google Scholar 

  10. Zhang, M., Zhou, Z.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)

    Article  Google Scholar 

  11. Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  12. Zhang, M., Zhou, Z.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)

    Article  Google Scholar 

  13. Elisseeff, A., Weston, J.: A kernel method for multilabelled classification. In: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, pp. 681–687 (2001)

    Google Scholar 

  14. Frnkranz, J., Hllermeier, E., Menca, E., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)

    Article  Google Scholar 

  15. Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2011)

    Article  Google Scholar 

  16. Geng, X.: Label distribution learning. IEEE Trans. Knowl. Data Eng. 28(7), 1734–1748 (2016)

    Article  Google Scholar 

  17. Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  18. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)

    Article  Google Scholar 

  19. He, X., Niyogi, P.: Locality preserving projections. Adv. Neural Inf. Process. Syst. 16(1), 86–197 (2004)

    Google Scholar 

  20. Zhang, Y., Zhang, Z., Qin, J., Li, F.: Semi-supervised local multi-manifold Isomap by linear embedding for feature extraction. Pattern Recogn. 76, 662–678 (2018)

    Article  Google Scholar 

  21. Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7(1), 2399–2434 (2006)

    MathSciNet  MATH  Google Scholar 

  22. Belkin, M., Niyogi, P.: On manifold regularization. Neurocomputing 73(10–12), 2203–2216 (2010)

    Google Scholar 

  23. Chen, L., Tsang, I., Xu, D.: Laplacian embedded regression for scalable manifold regularization. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 902–915 (2012)

    Article  Google Scholar 

  24. Zhang, Z., Li, F., Zhao, M., Zhang, L., Yan, S.: Robust neighborhood preserving projection by nuclear/L2,1-norm regularization for image feature extraction. IEEE Trans. Image Process. 26(4), 1607–1622 (2017)

    Article  MathSciNet  Google Scholar 

  25. Pahikkala, T., Airola, A.: RLscore: regularized least-squares learners. J. Mach. Learn. Res. 17, 1–5 (2016)

    MathSciNet  MATH  Google Scholar 

  26. Zhang, Z., Chow, T., Zhao, M.: Trace ratio optimization-based semi-supervised nonlinear dimensionality reduction for marginal manifold visualization. IEEE Trans. Knowl. Data Eng. 25(5), 1148–1161 (2013)

    Article  Google Scholar 

  27. Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. 26(1), 313–338 (2004)

    Article  MathSciNet  Google Scholar 

  28. Tuia, D., Verrelst, J., Alonso, L., Prez-Cruz, F., Camps-Valls, G.: Multioutput support vector regression for remote sensing biophysical parameter estimation. IEEE Geosci. Remote Sens. Lett. 8(4), 804–808 (2011)

    Article  Google Scholar 

  29. Prez-Cruz, F., Navia-Vazquez, A., Alarcon-Diana, P., Artes-Rodriguez, A.: An IRWLS procedure for SVR. In: European Signal Processing Conference, pp. 1–4 (2000)

    Google Scholar 

  30. Tan, C., Chen, C., Guan, J.: A nonlinear dimension reduction method with both distance and neighborhood preservation. In: Wang, M. (ed.) KSEM 2013. LNCS (LNAI), vol. 8041, pp. 48–63. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39787-5_5

    Chapter  Google Scholar 

  31. Tenenbaum, J., De Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  32. Mulan: Multi-label datasets for Multi-Label Learning[EB/OL]. http://mulan.sourceforge.net/datasets-mlc.html. Accessed 01 Feb 2018

  33. Zhang, M., Peña, J., Robles, V.: Feature selection for multi-label naive Bayes classification. Inf. Sci. 179(19), 3218–3229 (2009)

    Article  Google Scholar 

  34. Zhang, Q., Zhong, Y., Zhang, M.: Feature-induced labeling information enrichment for multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

Download references

Acknowledgements

This work is supported by National Natural Science Foundation of China (41471371, 61702270), the Project funded by China Postdoctoral Science Foundation under Grant. 2017M621592 and the open project foundation of Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Grant No. IIPL-2016-009.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, China

    Chao Tan & Genlin Ji

Authors
  1. Chao Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Genlin Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao Tan .

Editor information

Editors and Affiliations

  1. Southeast University, Nanjing, China

    Xin Geng

  2. University of Tasmania, Hobart, Tasmania, Australia

    Byeong-Ho Kang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tan, C., Ji, G. (2018). DKE-RLS: A Manifold Reconstruction Algorithm in Label Spaces with Double Kernel Embedding-Regularized Least Square. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97304-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97303-6

  • Online ISBN: 978-3-319-97304-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics