Advertisement

Applied Intelligence

, Volume 49, Issue 5, pp 1708–1723 | Cite as

Sparse and low-rank representation for multi-label classification

  • Zhi-Fen He
  • Ming YangEmail author
Article
  • 70 Downloads

Abstract

Multi-label learning deals with the problem where each instance may be associated with multiple labels simultaneously, and how to discover and exploit the label correlations is one of important research issues. In this paper, we propose a novel sparse and low-rank representation-based method for multi-label classification (SLMLC), which can automatically exploit the asymmetric correlations among labels while learning the model parameters in a unified learning framework. More specifically, we assume that the weight matrix is divided into a sparse matrix and a low-rank matrix, where the sparse and low-rank matrices are utilized to capture the specific features that are relevant to each label and the shared feature subspace among all labels, respectively. Then, we integrate multi-label classification and label correlations into a joint learning framework to learn the correlations among labels and the model parameters simultaneously. Lastly, the formulation is transformed into its convex surrogate due to its non-convexity, and we solve it by developing an alternating iterative method. Experimental results on fifteen data sets in terms of six evaluation criteria show that SLMLC achieves superior performance compared to the state-of-the-art multi-label classification algorithms.

Keywords

Multi-label classification Label correlations Sparse representation Low-rank representation 

Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China under Grants 61876087, 61502058, 61432008, the Science and Technology Research Project of Jiangxi Provincial Education Department under Grant GJJ151262, Natural Science Foundation of Educational Committee of Jiangsu Province under Grant 15KJB520002, and the Social Science Research Project of Pingxiang under Grant 2017XW02. The authors would like to thank the anonymous reviewers and the editors for their constructive and valuable comments.

References

  1. 1.
    Zhang ML, Zhou ZH (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26 (8):1819–1837CrossRefGoogle Scholar
  2. 2.
    Schapire RE, Singer Y (2000) Boostexter: a boosting-based system for text categorization. Mach Learn 39 (2/3):135–168CrossRefzbMATHGoogle Scholar
  3. 3.
    Zhang ML, Zhou ZH (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18(10):1338–1351CrossRefGoogle Scholar
  4. 4.
    Yan Y, Wang Y, et al., Gao WC (2018) LSTM: Multi-label ranking for document classification. Neural Process Lett 47(1)):117–138Google Scholar
  5. 5.
    Boutell MR, Luo J, Luo JB, Shen XP, Brown CM (2004) Learning multi-label scene classification. Pattern Recogn 37(9):1757–1771CrossRefGoogle Scholar
  6. 6.
    Wang C, Yan S, Zhang L, Zhang HJ (2009) Multi-label sparse coding for automatic image annotation. In: IEEE conference on computer vision and pattern recognition, Miami, pp 1643– 1650Google Scholar
  7. 7.
    Wang J, Yang Y, Mao JH et al (2016) Cnn-rnn: a unified framework for multi-label image classification. In: IEEE conference on computer vision and pattern recognition, pp 2285–2294Google Scholar
  8. 8.
    Tan QY, et al., Liu YZ, Chen X (2017) Multi-Label classification based on low rank representation for image annotation. Remote Sens 9(2):109Google Scholar
  9. 9.
    Qi GJ, Hua XS, Rui Y, et al. (2007) Correlative multi-label video annotation. In: Proceedings of the 15th ACM international conference on multimedia, Augsburg, pp 17–26Google Scholar
  10. 10.
    Janwe NJ, Bhoyar KK (2018) Multi-label semantic concept detection in videos using fusion of asymmetrically trained deep convolutional neural networks and foreground driven concept co-occurrence matrix. Appl Intell 48:2047–2066CrossRefGoogle Scholar
  11. 11.
    Trohidis K, Tsoumakas G, Kalliris G, Vlahavas IP (2008) Multilabel classification of music into emotions. In: Proceedings of the 9th international conference on music information retrieval, Philadephia, pp 325–330Google Scholar
  12. 12.
    Zhang ML, Zhang K (2010) Multi-label learning by exploiting label dependency. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, pp 999–1008Google Scholar
  13. 13.
    Gu Q, Li Z, Han J (2011) Correlated multi-label feature selection. In: Proceedings of the 20th ACM international conference on information and knowledge management, Glasgow, pp 1087–1096Google Scholar
  14. 14.
    Zhang Y, Yeung DY (2013) Multilabel relationship learning. ACM Trans Knowl Discov Data 7(2):1–30CrossRefGoogle Scholar
  15. 15.
    Bi W, James TK (2014) Multilabel classification with label correlations and missing labels. In: Proceedings of the Twenty-Eighth AAAI conference on artificial intelligence, Quebec, pp 1680–1686Google Scholar
  16. 16.
    Huang SJ, Yu Y, Zhou ZH (2012) Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, Beijing, pp 525–533Google Scholar
  17. 17.
    He ZF, Yang M, Liu HD (2014) Joint learning of multi-label classification and label correlations. J Soft 25(9):1967–1981. (in Chinese)zbMATHGoogle Scholar
  18. 18.
    Xu LL, et al., Wang Z, Shen ZF (2014) Learning low-rank label correlations for multi-label classification with missing labels. In: IEEE international conference on data mining (ICDM), Shenzhen, pp 1067–1072Google Scholar
  19. 19.
    He ZF, Yang M, Liu HD (2015) Multi-task joint feature selection for multi-label classification. Chin J Electron 24(CJE-2):281–287CrossRefGoogle Scholar
  20. 20.
    Elisseeff A, Weston J (2001) A kernel method for multi-labelled classification. In: Proceedings of the 14th conference on neural information processing systems (NIPS2001), Vancouver, pp 681–687Google Scholar
  21. 21.
    Zhang ML, Pena JM, Robles V (2009) Feature selection for multi-label naive Bayes classification. Inf Sci 179(19):3218–3229CrossRefzbMATHGoogle Scholar
  22. 22.
    Hullermeier E, Furnkranz J, Cheng W, Brinker K (2008) Label ranking by learning pairwise preferences. Artif Intell 172(16):1897–1916MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Furnkranz J, Hullermeier E, et al. (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153CrossRefGoogle Scholar
  24. 24.
    Read J, Pfahringer B, Holmes G, et al. (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359MathSciNetCrossRefGoogle Scholar
  25. 25.
    Tsoumakas G, Katakis I, Vlahavas I (2008) Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of ECML/PKDD 2008 workshop on mining multidimensional data, Antwerp, pp 30–44Google Scholar
  26. 26.
    Tsoumakas G, Katakis I, Vlahavas I (2011) Random k-labelsets for multilabel classification. IEEE Trans Knowl Data Eng 23(7):1079–1089CrossRefGoogle Scholar
  27. 27.
    Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Dai H, Srikant R, Zhang C (eds) Lecture notes in artificial intelligence 3056. Springer, Berlin, pp 22–30Google Scholar
  28. 28.
    Ghamrawi N, Mccallum A (2005) Collective multilabel classification. In: Proceedings of the 14th ACM international conference on information and knowledge management, Bremen, pp 195– 200Google Scholar
  29. 29.
    Chen G, Song YQ, Wang F, et al. (2008) Semi-supervised multi-label learning by solving a sylvester equation. In: SIAM conference on data mining, Atlanta, pp 410–419Google Scholar
  30. 30.
    Weng W, Lin YJ, Wu SX, et al. (2018) Multi-label learning based on label-specific features and local pairwise label correlation. Neurocomputing 273:385–394CrossRefGoogle Scholar
  31. 31.
    Zhu Y, Kwok JT, Zhou ZH (2017) Multi-label learning with global and local label correlation. IEEE Transactions on Knowledge and Data Engineering. arXiv:1704.01415
  32. 32.
    Guo Y, Gu SC (2011) Multi-label classification using conditional dependency networks. In: Walsh T (ed) Proceedings of the 22nd international joint conference on artificial intelligence, AAAI Press, pp 1300–1305Google Scholar
  33. 33.
    Guo Y, Xue W (2013) Probabilistic multi-label classification with sparse feature learning. In: Rossi F (ed) Proceedings of the 23rd international joint conference on artificial intelligence, AAAI Press, pp 1373–1379Google Scholar
  34. 34.
    Zhang ML (2011) LIFT: multi-label learning with label-specific features. In: Walsh T (ed) Proceedings of the 22nd international joint conference on artificial intelligence, Barcelona, pp 1609–1614Google Scholar
  35. 35.
    Ji S, Ye J (2009) Linear dimensionality reduction for multi-label classification. In: Proceedings of the 21st international joint conference on artificial intelligence, California, pp 1077–1082Google Scholar
  36. 36.
    Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048CrossRefzbMATHGoogle Scholar
  37. 37.
    Read J (2008) A pruned problem transformation method for multi-label classification. In: Proceedings of New Zealand computer science research student conference, Christchurch, pp 143-150Google Scholar
  38. 38.
    Ji S, Tang L, Yu S, Ye J (2010) A shared-subspace learning framework for multi-label classification. ACM Trans Knowl Discovery Data 4(2):1–29CrossRefGoogle Scholar
  39. 39.
    Chen J, Liu J, Ye J (2012) Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Trans Knowl Discovery Data 5(4):1–31CrossRefGoogle Scholar
  40. 40.
    Gutta S, Cheng Q (2013) Joint multitask feature learning and classifier design. In: Proceedings of the 47th annual conference on information sciences and systems, pp 1–5Google Scholar
  41. 41.
    Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  42. 42.
    Fazel M, Hindi H, Boyd S (2001) A rank minimization heuristic with application to minimum order system approximation. In: Proceedings of the 2001 American control conference, pp 4734–4739Google Scholar
  43. 43.
    Ji S, Ye J (2009) An accelerated gradient method for trace normminimization. In: Proceedings of the 26th annual international conference on machine learning, Montreal, pp 457–464Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingPeople’s Republic of China
  2. 2.School of Information and Computer EngineeringPingxiang UniversityJiangxiPeople’s Republic of China

Personalised recommendations