Skip to main content

Advertisement

SpringerLink
Log in

Learning shared subspace regularization with linear discriminant analysis for multi-label action recognition

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Human action recognition under complex environment is a challenging work, while in deep learning and in these specific difficulty recognition tasks, the multi-label linear discriminant analysis (MLDA) is already utilized. As is known to all, MLDA is used for dimensionality feature reduction. Nevertheless, MLDA contains the eigendecomposition of dense matrices, which will cost a huge money on a high dimension computing. In this study, we demonstrate that the MLDA formula is able to equivalent to the least squares problem, which greatly reduces the computation and size of high-dimensional datasets. In addition, it is found that introducing attractive regularization technique into the classical least squares strategy can improve the robustness. The established equivalence relationship is proved by laboratory results on three action datasets. In addition, through comparing with some typical and recent algorithms, the superiority of our constructed model is also demonstrated.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Akbarnejad AH, Baghshah MS (2019) An efficient semi-supervised multi-label classifier capable of handling missing labels. IEEE Trans Knowl Data Eng 32:229–242

    Article  Google Scholar 

  2. Barnard K, Duygulu P, Forsyth DM, De Freitas N, Blei D, Jordan MI (2003) Matching words and pictures. J Mach Learn Res 3:1107–1135

    MATH  Google Scholar 

  3. Barutcuoglu Z, Schapire RE, Troyanskaya OG (2006) Hierarchical multi-label prediction of gene function. Bioinformatics 22(7):830–836

    Article  Google Scholar 

  4. Bian W, Tao D (2012) Cross-domain human action recognition. IEEE Trans Syst Man Cybern B Cybern 42:298–307

    Article  Google Scholar 

  5. Bin L, Guo W, Xiong N, Chen G, Athanasios VV, Zhang H (2016) A pretreatment workflow scheduling approach for big data applications in multi-cloud environments. Trans Netw Serv Manag 13:581–594

    Article  Google Scholar 

  6. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159

    Article  Google Scholar 

  7. Cai D, He X, Han J (2008) SRDA: an efficient algorithm for large-scale discriminant analysis. IEEE Trans Knowl Data Eng 20(1):1–12

    Article  Google Scholar 

  8. Caetano C, dos Santos JA, Schwartz WR (2017) Optical flow co-occurrence matrices: a novel spatiotemporal feature descriptor. In: Proceedings ICPR, pp 1947–1952

  9. Chen B, Lam W, Tsang IW, Wong T-L (2013) Discovering low-rank shared concept space for adapting text mining models. IEEE Trans Pattern Anal Mach Intell 35(6):1284–1297

    Article  Google Scholar 

  10. Chen J, Shan S, He C (2010) Wld: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32:1705–1720

    Article  Google Scholar 

  11. Chen CM, Wang K-H, Yeh K-H, Xiang B, Wu T-Y (2019) Attacks and solutions on a three-party password-based authenticated key exchange protocol for wireless communications. J Ambient Intell Human Comput 10(8):3133–3142

    Article  Google Scholar 

  12. Ciarelli PM, Oliveira E, Salles EOT (2014) Multi-label incremental learning applied to web page categorization. Neural Comput Appl 24(6):1403–1419

    Article  Google Scholar 

  13. De la Torre F (2012) A least-squares framework for component analysis. IEEE Trans Pattern Anal Mach Intell 34(6):1041–1055

    Article  Google Scholar 

  14. Dembczyński K, Waegeman W, Cheng W, Hüllermeier E (2012) On label dependence and loss minimization in multi-label classification. Mach Learn 88(1–2):5–45

    Article  MathSciNet  MATH  Google Scholar 

  15. Duda RO, Hart PE, Stork DG (2001) Pattern classification, vol 2. Wiley, New York

    MATH  Google Scholar 

  16. Fang Z, Fei F, Fang Y, Lee C, Xiong N, Shu L, Chen S (2016) Abnormal event detection in crowded scenes based on deep learning. Multimed Tools Appl 75(22):14617–14639

    Article  Google Scholar 

  17. Fukunaga K (1990) Introduction to statistical pattern recognition. Academic Press Professional, London

    MATH  Google Scholar 

  18. Golub GH, Van Loan CF (1996) Matrix computation. The Johns Hopkins University Press, Baltimore

    MATH  Google Scholar 

  19. Guo W, Xiong N, Vasilakos AV, Chen G, Yu C (2012) Distributed k-connected faulttolerant topology control algorithms with PSO in future autonomic sensor systems. Int J Sens Netw 12(1):53–62

    Article  Google Scholar 

  20. Guo W, Liu G, Chen G, Peng S (2014) A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning. Front Comput Sci 8(2):203–216

    Article  MathSciNet  Google Scholar 

  21. Guo W, Li J, Chen G, Niu Y, Chen C (2015) A PSO-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks. Trans Parallel Distrib Syst 26:3236–3249

    Article  Google Scholar 

  22. Guo Y, Liu Y, Bakker EM, Guo Y, Lew MS (2018) CNN-RNN: a large-scale hierarchical image classification framework. Multimed Tools Appl 77:10251–10271

    Article  Google Scholar 

  23. Hara K, Kataoka H, Satoh Y (2017) Learning spatio-temporal features with 3d residual networks for action recognition. In: Proceedings of the IEEE international conference on computer vision, pp 3154–3160

  24. Hardoon DR, Szedmak S, Shawe-Taylor J (2004) Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16(12):2639–2664

    Article  MATH  Google Scholar 

  25. Hastie T, Tibshirani R, Friedman JH (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York

    Book  MATH  Google Scholar 

  26. Hotelling H (1936) Relations between two sets of variates. Biometrika 28(3–4):321–377

    Article  MATH  Google Scholar 

  27. Indyk W, Kajdanowicz T, Kazienko P (2013) Relational large scale multi-label classification method for video categorization. Multimed Tools Appl 65:63–74

    Article  Google Scholar 

  28. Ji S, Tang L, Yu S, Ye J (2010) A shared-subspace learning framework for multi-label classification. ACM Trans Knowl Discov Data (TKDD) 4(2):8

    Google Scholar 

  29. Jing L, Shen C, Yang L (2017) Multi-label classification by semi-supervised singular value decomposition. IEEE Trans Image Process 12:4612–4625

    Article  MathSciNet  MATH  Google Scholar 

  30. Khan FS, van de Weijier J, Anwer RM (2018) Scale coding bag of deep features for human attribute and action recognition. Mach Vis Appl 29:55–71

    Article  Google Scholar 

  31. Li J, Xiong N, Park JH, Liu C, Shihua MA, Cho SE (2012) Intelligent model design of cluster supply chain with horizontal cooperation. J Intell Manuf 23(4):917–931

    Article  Google Scholar 

  32. Liu G, Guo W, Li R, Niu Y, Chen G (2015) Xgrouter: high-quality global router in x-architecture with particle swarm optimization. Front Comput Sci 9(4):576–594

    Article  Google Scholar 

  33. Liu G, Guo W, Niu Y, Chen G, Huang X (2015) A PSO-based timing-driven octilinear steiner tree algorithm for VLSI routing considering bend reduction. Soft Comput 19(5):1153–1169

    Article  MATH  Google Scholar 

  34. Liu G, Huang X, Guo W, Niu Y, Chen G (2014) Multilayer obstacle-avoiding x-architecture Steiner minimal tree construction based on particle swarm optimization. IEEE Trans Cybern 45(5):1003–1016

    Google Scholar 

  35. Liu J, Ji S, Ye J (2009) SLEP: sparse learning with efficient projections. Arizona State University, Tucson

    Google Scholar 

  36. Liu L, Shao L, Li X, Lu K (2015) Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans Cybern 46(1):158–170

    Article  Google Scholar 

  37. Lu H, Fang G, Shao X, Li X (2012) Segmenting human from photo images based on a coarse-to-fine scheme. IEEE Trans Syst Man Cybern B Cybern 42:889–899

    Google Scholar 

  38. Luo F, Guo W, Yu Y, Chen G (2016) A multi-label classification algorithm based on kernel extreme learning machine. Neurocomputing 260:313–320

    Article  Google Scholar 

  39. Ma Z, Nie F, Yang Y, Uijlings JRR, Sebe N (2012) Web image annotation via subspace-sparsity collaborated feature selection. IEEE Trans Multimed 14(4):1021–1030

    Article  Google Scholar 

  40. Monay F, Gatica-Perez D (2007) Modeling semantic aspects for cross-media image indexing. IEEE Trans Pattern Anal Mach Intell 29(10):1802–1817

    Article  Google Scholar 

  41. Niu Y, Chen J, Guo W (2018) Meta-metric for saliency detection evaluation metrics based on application preference. Multimed Tools Appl 77:26351–26369

    Article  Google Scholar 

  42. Pan JS, Hu P, Chu S-C (2019) Novel parallel heterogeneous metaheuristic and its communication strategies for the prediction of wind power. Processes 7(11):845

    Article  Google Scholar 

  43. Park CH, Lee M (2008) On applying linear discriminant analysis for multi-labeled problems. Pattern Recognit Lett 29(7):878–887

    Article  Google Scholar 

  44. Roth V, Fischer B (2007) Improved functional prediction of proteins by learning kernel combinations in multilabel settings. BMC Bioinform 8(Suppl 2):S12

    Article  Google Scholar 

  45. Sang Y, Shen H, Tan Y, Xiong N (2006) Efficient protocols for privacy preserving matching against distributed datasets. In: International conference on information and communications security, pp 210–227

  46. Song L, Liu J, Qian B, Sun M, Yang K, Sun M, Abbas S (2018) A deep multi-model CNN for multi-instance multi-label image classification. IEEE Trans Image Process 27:6025–6038

    Article  MathSciNet  Google Scholar 

  47. Sun L, Ji S, Ye J (2008) Hypergraph spectral learning for multi-label classification. In: Knowledge Discovery and Data Mining, pp 668–676

  48. Sun L, Ji S, Ye J (2011) Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis. IEEE Trans Pattern Anal Mach Intell 33(1):194–200

    Article  Google Scholar 

  49. Tai F, Lin H-T (2012) Multilabel classification with principal label space transformation. Neural Comput 24(9):2508–2542

    Article  MathSciNet  MATH  Google Scholar 

  50. Tang L, Rajan S, Narayanan VK (2009) Large scale multi-label classification via metalabeler. In: Proceedings of the 18th International Conference on World Wide Web. ACM, pp 211–220

  51. Ueda N, Saito K (2002) Parametric mixture models for multi-labeled text. In: Advances in neural information processing systems, pp 721–728

  52. Wang S, Guo W (2017) Sparse multi-graph embedding for multimodal feature representation. Trans Multimed 19:1454–1466

    Article  Google Scholar 

  53. Wang H, Yuan C, Hu W (2012) Supervised class-specific dictionary learning for sparse modeling in action recognition. Pattern Recognit 45:3902–3911

    Article  Google Scholar 

  54. Wang H, Ding C, Huang H (2010) Multi-label linear discriminant analysis. In: ECCV 2010, pp 126–139. Springer

  55. Wang X, Li S, Zhao S, Xia Z (2017) A vanet privacy protection scheme based on fair blind signature and secret sharing algorithm. Automatika 58(3):287–294

    Article  Google Scholar 

  56. Wang X, Li S, Zhao S, Xia Z, Bai L (2017) A vehicular ad hoc network privacy protection scheme without a trusted third party. Int J Distrib Sens Netw 13(12):1550147717743696

    Google Scholar 

  57. Wang X, Song W, Zhang B, Mausler B, Jiang F (2019) An early warning system for curved road based on ov7670 image acquisition and stm32. CMC-Comput Mater Continua 59(1):135–147

    Article  Google Scholar 

  58. Wang X, Zeng P, Patterson N, Jiang F, Doss R (2019) An improved authentication scheme for internet of vehicles based on blockchain technology. IEEE Access 7:45061–45072

    Article  Google Scholar 

  59. Weiping Z, Guo W, Yu Z, Xiong H (2018) Multitask allocation to heterogeneous participants in mobile crowd sensing. Wirel Commun Mobile Comput 2018:721–731

    Google Scholar 

  60. Xiang L, Shen X, Qin J, Hao W (2019) Discrete multi-graph hashing for large-scale visual search. Neural Process Lett 49(3):1055–1069

    Article  Google Scholar 

  61. Xing H, Liu G, Guo W, Niu Y, Chen G (2015) Obstacle-avoiding algorithm in x-architecture based on discrete particle swarm optimization for VLSI design. ACM Trans Des Autom Electron Syst 20:24–28

    Google Scholar 

  62. Xing H, Guo W, Liu G, Chen G (2016) FH-OAOS: a fast 4-step heuristic for obstacle-avoiding octilinear architecture router construction. ACM Trans Des Autom Electron Syst 21:30–48

    Google Scholar 

  63. Yang Y, Liu X, Zheng X, Rong C, Guo W (2018) Efficient traceable authorization search system for secure cloud storage. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2018.2820714

    Article  Google Scholar 

  64. Ye J (2007) Least squares linear discriminant analysis. In: Proceedings of the 24th international conference on machine learning, pp 1087–1093. ACM

  65. Yongli C, Jiang H, Wang F, Hua Y, Feng D, Guo W, Wu Y (2019) Using high-bandwidth networks efficiently for fast graph computation. Trans Parallel Distrib Syst 30:1170C–1183

    Article  Google Scholar 

  66. Yuchang M, Xing L, Lina Y-K, Guo W (2018) Efficient analysis of repairable computing systems subject to scheduled checkpointing. Trans Depend Secure Comput 2018:286–293

    Google Scholar 

  67. Zhang M-L, Zhou Z-H (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit 40(7):2038–2048

    Article  MATH  Google Scholar 

  68. Zhang T, Jia W, Gong C, Sun J, Song X (2018) Semi-supervised dictionary learning via local sparse constraints for violence detection. Pattern Recognit Lett 107:98–104

    Article  Google Scholar 

  69. Zhang T, Jia W, He X, Yang J (2017) Discriminative dictionary learning with motion weber local descriptor for violence detection. IEEE Trans Circuits Syst Video Technol 27(3):696–709

    Article  Google Scholar 

  70. Zhang T, Jia W, Yang J (2015) Sparse coding-based spatiotemporal saliency for action recognition. In: Proceedings ICIP, pp 2045–2049

  71. Zhang Y, Zhou Z-H (2010) Multilabel dimensionality reduction via dependence maximization. ACM Trans Knowl Discov ata (TKDD) 4(3):14

    Google Scholar 

  72. Zhu F, Li H, Ouyang W, Yu N, Wang X (2017) Learning spatial regularization with image-level supervisions for multi-label image classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp 2027–2036

Download references

Acknowledgements

This paper is supported by Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources (No. KF-2018-03-065), Dongguan Social Science and Technology Development Project (2017507156388), 2017 Guangdong Provincial Department of Education Youth Innovation Talents Project (2017GkQNCX119), 2018 Dongguan Polytechnic Political School Enterprise Cooperation Project (Zheng 201805), 2017 Guangdong Province University’s Characteristic Innovation (Natural Science) Project (2017GKTSCX101).

Author information

Authors and Affiliations

  1. Dongguan Polytechnic, Dongguan, Guangdong, China

    Jianxin Li

  2. School of Nursing, Taihu University of Wuxi, Qianrong street No. 68, Wuxi, 214064, Jiangsu Province, China

    Minjie Liu

  3. Laboratory of Pattern Recognition and Computational Intelligence, JiangNan University, WuXi, China

    Dongliang Ma

  4. Dalingshan People’s Hospital, Dongguan, Guangdong, China

    Jinyu Huang

  5. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China

    Min Ke

  6. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen, China

    Tao Zhang

Authors
  1. Jianxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Minjie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongliang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinyu Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Min Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianxin Li.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Liu, M., Ma, D. et al. Learning shared subspace regularization with linear discriminant analysis for multi-label action recognition. J Supercomput 76, 2139–2157 (2020). https://doi.org/10.1007/s11227-020-03149-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03149-6

Keywords

Search

Navigation