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Latent Topic-Aware Multi-label Classification

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12359))

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Abstract

In real-world applications, data are often associated with different labels. Although most extant multi-label learning algorithms consider the label correlations, they rarely consider the topic information hidden in the labels, where each topic is a group of related labels and different topics have different groups of labels. In our study, we assume that there exists a common feature representation for labels in each topic. Then, feature-label correlation can be exploited in the latent topic space. This paper shows that the sample and feature exaction, which are two important procedures for removing noisy and redundant information encoded in training samples in both sample and feature perspectives, can be effectively and efficiently performed in the latent topic space by considering topic-based feature-label correlation. Empirical studies on several benchmarks demonstrate the effectiveness and efficiency of the proposed topic-aware framework.

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Notes

  1. 1.

    https://lear.inrialpes.fr/people/guillaumin/data.php.

  2. 2.

    http://mulan.sourceforge.net/datasets-mlc.html.

References

  1. Bhatia, K., Jain, H., Kar, P., Varma, M., Jain, P.: Sparse local embeddings for extreme multi-label classification. In: Advances in Neural Information Processing Systems, pp. 730–738 (2015)

    Google Scholar 

  2. Cai, X., Nie, F., Huang, H.: Exact top-k feature selection via l2, 0-norm constraint. In: 23rd International Joint Conference on Artificial Intelligence (2013)

    Google Scholar 

  3. Chang, X., Nie, F., Yang, Y., Huang, H.: A convex formulation for semi-supervised multi-label feature selection. In: 28th AAAI Conference on Artificial Intelligence (2014)

    Google Scholar 

  4. Duygulu, P., Barnard, K., de Freitas, J.F.G., Forsyth, D.A.: Object recognition as machine translation: learning a lexicon for a fixed image vocabulary. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 97–112. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47979-1_7

    Chapter  Google Scholar 

  5. Elhamifar, E., Sapiro, G., Sastry, S.S.: Dissimilarity-based sparse subset selection. IEEE Trans. Pattern Anal. Mach. Intell. 38(11), 2182–2197 (2016). https://doi.org/10.1109/TPAMI.2015.2511748

    Article  Google Scholar 

  6. Gibaja, E., Ventura, S.: A tutorial on multilabel learning. ACM Comput. Surv. (CSUR) 47(3), 52 (2015)

    Article  Google Scholar 

  7. Guo, Y., Xue, W.: Probabilistic multi-label classification with sparse feature learning. In: IJCAI, pp. 1373–1379 (2013)

    Google Scholar 

  8. Hoyer, P.O.: Modeling receptive fields with non-negative sparse coding. Neurocomputing 52, 547–552 (2003)

    Article  Google Scholar 

  9. Huang, J., Li, G., Huang, Q., Wu, X.: Learning label-specific features and class-dependent labels for multi-label classification. IEEE Trans. Knowl. Data Eng. 28(12), 3309–3323 (2016)

    Article  Google Scholar 

  10. Huang, J., Li, G., Huang, Q., Wu, X.: Joint feature selection and classification for multilabel learning. IEEE Trans. Cybern. 48(3), 876–889 (2017)

    Article  Google Scholar 

  11. Huang, S.J., Zhou, Z.H.: Multi-label learning by exploiting label correlations locally. In: 26th AAAI Conference on Artificial Intelligence (2012)

    Google Scholar 

  12. Jian, L., Li, J., Shu, K., Liu, H.: Multi-label informed feature selection. In: IJCAI, pp. 1627–1633 (2016)

    Google Scholar 

  13. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)

    Article  Google Scholar 

  14. Liu, H., Li, X., Zhang, S.: Learning instance correlation functions for multilabel classification. IEEE Trans. Cybern. 47(2), 499–510 (2017)

    Article  Google Scholar 

  15. Liu, H., Zhang, S., Wu, X.: Mlslr: multilabel learning via sparse logistic regression. Inf. Sci. 281, 310–320 (2014)

    Article  MathSciNet  Google Scholar 

  16. Liu, W., Tsang, I.W.: Large margin metric learning for multi-label prediction. In: 29th AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  17. Ma, J., Zhang, H., Chow, T.W.S.: Multilabel classification with label-specific features and classifiers: a coarse- and fine-tuned framework. IEEE Trans. Cybern. 1–15 (2019). https://doi.org/10.1109/TCYB.2019.2932439

  18. Ma, J., Chow, T.W.: Topic-based algorithm for multilabel learning with missing labels. IEEE Trans. Neural Netw. Learn. Syst. 30(7), 2138–2152 (2018)

    Article  Google Scholar 

  19. Ma, J., Chow, T.W., Zhang, H.: Semantic-gap-oriented feature selection and classifier construction in multilabel learning. IEEE Trans. Cybern. 1–15 (2020)

    Google Scholar 

  20. Nie, F., Huang, H., Cai, X., Ding, C.H.: Efficient and robust feature selection via joint \(l_{2,1}\)-norms minimization. In: Advances in Neural Information Processing Systems, pp. 1813–1821 (2010)

    Google Scholar 

  21. Pang, T., Nie, F., Han, J., Li, X.: Efficient feature selection via \(l_\{2,0\}\)-norm constrained sparse regression. IEEE Trans. Knowle. Data Eng. 31(5), 880–893 (2018)

    Article  Google Scholar 

  22. Ren, J., et al.: Learning hybrid representation by robust dictionary learning in factorized compressed space. IEEE Trans. Image Process. 29, 3941–3956 (2020)

    Article  MathSciNet  Google Scholar 

  23. Shen, X., Liu, W., Tsang, I.W., Sun, Q.S., Ong, Y.S.: Multilabel prediction via cross-view search. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4324–4338 (2017)

    Article  Google Scholar 

  24. Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehouse. Min. 3(3), 1–13 (2006)

    Google Scholar 

  25. Wei, K., Iyer, R., Bilmes, J.: Submodularity in data subset selection and active learning. In: International Conference on Machine Learning, pp. 1954–1963 (2015)

    Google Scholar 

  26. Zhang, H., Sun, Y., Zhao, M., Chow, T.W., Wu, Q.J.: Bridging user interest to item content for recommender systems: an optimization model. IEEE Trans. Cybern. 50, 4268–4280 (2019)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Zhang, R., Nie, F., Li, X.: Self-weighted supervised discriminative feature selection. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3913–3918 (2018)

    Article  Google Scholar 

  30. Zhang, Z., et al.: Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3798–3814 (2017)

    Article  MathSciNet  Google Scholar 

  31. Zhang, Z., Zhang, Y., Liu, G., Tang, J., Yan, S., Wang, M.: Joint label prediction based semi-supervised adaptive concept factorization for robust data representation. IEEE Trans. Knowl. Data Eng. 32(5), 952–970 (2019)

    Article  Google Scholar 

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Authors and Affiliations

  1. City University of Hong Kong, Hong Kong, China

    Jianghong Ma

  2. The Hong Kong University of Science and Technology, Hong Kong, China

    Yang Liu

Authors
  1. Jianghong Ma
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  2. Yang Liu
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Correspondence to Jianghong Ma .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Ma, J., Liu, Y. (2020). Latent Topic-Aware Multi-label Classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12359. Springer, Cham. https://doi.org/10.1007/978-3-030-58568-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-58568-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58567-9

  • Online ISBN: 978-3-030-58568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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