Skip to main content
SpringerLink
Log in

Class-oriented and label embedding analysis dictionary learning for pattern classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Analysis dictionary learning (ADL) has obtained lots of research interest in sparse representation-based classification recent years, due to its flexibility and low complexity for out-of-sample representation. However, the discrimination of the dominant analysis dictionary is not fully explored and the manifold information is not inherited into analysis atoms for classification. To remedy the deficiencies, we present joint class-oriented and label embedding (COLE) constraints on the analysis dictionary for pattern classification. Specifically, the comprehensive class-oriented constraints on the analysis subdictionaries efficiently yield discriminative class-wise atoms and between-class separable representation for classification. The redundant atoms can be eliminated by orthogonal subdictionary constraints, leading to a robust and within-class compact analysis dictionary. Furthermore, the label embedding term of analysis atoms inherits the supervised manifold information of the training samples and guarantees an ideal block-diagonal representation. Finally, an computationally efficient alternating direction minimization algorithm is presented with iterative reweighted and closed-form solutions, which avoids the time-consuming multiplication of class-specific data samples and the subdictionaries. Extensive experiments on five benchmark databases demonstrate at least comparable or better classification accuracy and efficiency of the proposed model compared with state-of-the-art ADL models.

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
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

All data generated or analysed during this study are included in this published article (and its supplementary information files).

References

  1. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227. https://doi.org/10.1109/TPAMI.2008.79

    Article  Google Scholar 

  2. Ambati LS, El-Gayar O (2021) Human activity recognition: a comparison of machine learning approaches. J Midwest Assoc Inform Syst 1(4):49–60. https://doi.org/10.17705/3jmwa.000065

    Google Scholar 

  3. Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233–247. https://doi.org/10.1016/j.neunet.2021.10.021

    Article  Google Scholar 

  4. El-Gayar O, Ambati LS, Nawar N (2020) Wearables, artificial intelligence, and the future of healthcare. In: AI and Big Data’s potential for disruptive innovation. https://doi.org/10.4018/978-1-5225-9687-5.ch005. IGI Global, Hershey, pp 104–129

  5. Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: Which helps face recognition?. In: 2011 International conference on computer vision. https://doi.org/10.1109/ICCV.2011.6126277, pp 471–478

  6. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation. In: 2011 International conference on computer vision, pp 543–550. https://doi.org/10.1109/ICCV.2011.6126286

  7. Gurkan F, Cerkezi L, Cirakman O, Gunsel B (2021) Tdiot: target-driven inference for deep video object tracking. IEEE Trans Image Process 30:7938–7951. https://doi.org/10.1109/TIP.2021.3112010

    Article  Google Scholar 

  8. Sun S, Akhtar N, Song H, Mian A, Shah M (2021) Deep affinity network for multiple object tracking. IEEE Trans Pattern Anal Mach Intell 43 (1):104–119. https://doi.org/10.1109/TPAMI.2019.2929520

    Google Scholar 

  9. Ambati LS, El-Gayar O, Nevine N (2020) Influence of the digital divide and socio-economic factors on prevalence of diabetes. Issues in Information Systems 11(4):103–113. https://doi.org/10.48009/4_iis_2020_103-113

    Google Scholar 

  10. Ambati LS, El-Gayar OF, Nawar N (2021) Design principles for multiple sclerosis mobile self-management applications: a patient-centric perspective. In: Chan Y.E., Boudreau M., Aubert B., ParÉ G., Chin W. (eds) 27th Americas Conference on Information Systems, AMCIS 2021, Virtual Conference, August 9-13, 2021. Association for Information Systems

  11. Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852–870. https://doi.org/10.1016/j.ins.2021.08.042

    Article  MathSciNet  Google Scholar 

  12. Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 80(20):31401–31433. https://doi.org/10.1007/s11042-020-10486-4

    Article  Google Scholar 

  13. Yang L, Yang G, Wang K, Hao F, Yin Y (2021) Finger vein recognition via sparse reconstruction error constrained low-rank representation. IEEE Trans Inform Forens Secur 16:4869–4881. https://doi.org/10.1109/TIFS.2021.3118894

    Article  Google Scholar 

  14. Zhang X, Zhang F, Xu C (2022) Joint expression synthesis and representation learning for facial expression recognition. IEEE Trans Circuits Syst Video Technol 32(3):1681–1695. https://doi.org/10.1109/TCSVT.2021.3056098

    Article  Google Scholar 

  15. Zhang Z, Tran L, Liu F, Liu X (2022) On learning disentangled representations for gait recognition. IEEE Trans Pattern Anal Mach Intell 44(1):345–360. https://doi.org/10.1109/TPAMI.2020.2998790

    Article  Google Scholar 

  16. Jiang Z, Lin Z, Davis LS (2013) Label consistent k-svd: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664. https://doi.org/10.1109/TPAMI.2013.88

    Article  Google Scholar 

  17. Li Z, Lai Z, Xu Y, Yang J, Zhang D (2017) A locality-constrained and label embedding dictionary learning algorithm for image classification. IEEE Trans Neural Netw Learn Syst 28(2):278–293. https://doi.org/10.1109/TNNLS.2015.2508025

    Article  MathSciNet  Google Scholar 

  18. Aharon M, Elad M, Bruckstein A (2006) K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322. https://doi.org/10.1109/TSP.2006.881199

    Article  MATH  Google Scholar 

  19. Kong S, Wang D Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C (eds) (2012) A dictionary learning approach for classification: separating the particularity and the commonality. Springer, Berlin

  20. Zhang Q, Li B (2010) Discriminative k-svd for dictionary learning in face recognition. In: 2010 IEEE Computer society conference on computer vision and pattern recognition, pp. 2691–2698. https://doi.org/10.1109/CVPR.2010.5539989

  21. Rubinstein R, Peleg T, Elad M (2013) Analysis k-svd: a dictionary-learning algorithm for the analysis sparse model. IEEE Trans Signal Process 61 (3):661–677. https://doi.org/10.1109/TSP.2012.2226445

    Article  MathSciNet  MATH  Google Scholar 

  22. Ramirez I, Sprechmann P, Sapiro G (2010) Classification and clustering via dictionary learning with structured incoherence and shared features. In: 2010 IEEE Computer society conference on computer vision and pattern recognition, pp. 3501–3508. https://doi.org/10.1109/CVPR.2010.5539964

  23. Vu TH, Monga V (2017) Fast low-rank shared dictionary learning for image classification. IEEE Trans Image Process 26(11):5160–5175. https://doi.org/10.1109/TIP.2017.2729885

    Article  MathSciNet  MATH  Google Scholar 

  24. Guo J, Guo Y, Kong X, Zhang M, He R (2016) Discriminative analysis dictionary learning. In: Schuurmans D, Wellman MP (eds) Proceedings of the Thirtieth AAAI conference on artificial intelligence, February 12-17, 2016. AAAI Press, Phoenix, pp 1617–1623

  25. Tang W, Panahi A, Krim H, Dai L (2019) Analysis dictionary learning based classification: structure for robustness. IEEE Trans Image Process 28(12):6035–6046. https://doi.org/10.1109/TIP.2019.2919409

    Article  MathSciNet  MATH  Google Scholar 

  26. Hawe S, Kleinsteuber M, Diepold K (2013) Analysis operator learning and its application to image reconstruction. IEEE Trans Image Process 22 (6):2138–2150. https://doi.org/10.1109/TIP.2013.2246175

    Article  MathSciNet  MATH  Google Scholar 

  27. Shekhar S, Patel VM, Chellappa R (2014) Analysis sparse coding models for image-based classification. In: 2014 IEEE International conference on image processing (ICIP), pp. 5207–5211. https://doi.org/10.1109/ICIP.2014.7026054

  28. Ravishankar S, Bresler Y (2013) Learning sparsifying transforms. IEEE Trans Signal Process 61(5):1072–1086. https://doi.org/10.1109/TSP.2012.2226449

    Article  MathSciNet  MATH  Google Scholar 

  29. Wang Q, Guo Y, Guo J, Kong X (2018) Synthesis k-svd based analysis dictionary learning for pattern classification. Multimedia Tools Appl. 77(13):17023–17041. https://doi.org/10.1007/s11042-017-5269-6https://doi.org/10.1007/s11042-017-5269-6

    Article  Google Scholar 

  30. Du H, Zhang Y, Ma L, Zhang F (2021) Structured discriminant analysis dictionary learning for pattern classification. Knowl-Based Syst 216:106794. https://doi.org/10.1016/j.knosys.2021.106794

    Article  Google Scholar 

  31. Gu S, Zhang L, Zuo W, Feng X (2014) Projective dictionary pair learning for pattern classification. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates Inc

  32. Zhang Z, Jiang W, Qin J, Zhang L, Li F, Zhang M, Yan S (2018) Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier. IEEE Transactions on Neural Networks and Learning Systems 29(8):3798–3814. https://doi.org/10.1109/TNNLS.2017.2740224

    Article  MathSciNet  Google Scholar 

  33. Yang M, Chang H, Luo W (2017) Discriminative analysis-synthesis dictionary learning for image classification. Neurocomputing 219:404–411. https://doi.org/10.1016/j.neucom.2016.09.037

    Article  Google Scholar 

  34. Yang M, Chang H, Luo W, Yang J (2017) Fisher discrimination dictionary pair learning for image classification. Neurocomputing 269:13–20. https://doi.org/10.1016/j.neucom.2016.08.146

    Article  Google Scholar 

  35. Chen Z, Wu XJ, Kittler J (2021) Relaxed block-diagonal dictionary pair learning with locality constraint for image recognition. IEEE Transactions on Neural Networks and Learning Systems 1–15. https://doi.org/10.1109/TNNLS.2021.3053941

  36. Zhang Z, Sun Y, Wang Y, Zhang Z, Zhang H, Liu G, Wang M (2021) Twin-incoherent self-expressive locality-adaptive latent dictionary pair learning for classification. IEEE Transactions on Neural Networks and Learning Systems 32(3):947–961. https://doi.org/10.1109/TNNLS.2020.2979748

    Article  MathSciNet  Google Scholar 

  37. Li Z, Zhang Z, Wang S, Ma R, Lei F, Xiang D (2021) Structured analysis dictionary learning based on discriminative fisher pair. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03262-1

  38. Li Z, Ding S, Hayashi T, Li Y (2017) Analysis dictionary learning using block coordinate descent framework with proximal operators. Neurocomputing 239:165–180. https://doi.org/10.1016/j.neucom.2017.02.014https://doi.org/10.1016/j.neucom.2017.02.014

    Article  Google Scholar 

  39. Shao S, Xu R, Liu W, Liu BD, Wang YJ (2020) Label embedded dictionary learning for image classification. Neurocomputing 385:122–131. https://doi.org/10.1016/j.neucom.2019.12.071

    Article  Google Scholar 

  40. Wang J, Guo Y, Guo J, Li M, Kong X (2017) Synthesis linear classifier based analysis dictionary learning for pattern classification. Neurocomputing 238:103–113. https://doi.org/10.1016/j.neucom.2017.01.041

    Article  Google Scholar 

  41. Wang J, Guo Y, Guo J, Luo X, Kong X (2017) Class-aware analysis dictionary learning for pattern classification. IEEE Signal Process Lett 24 (12):1822–1826. https://doi.org/10.1109/LSP.2017.2734860

    Article  Google Scholar 

  42. Shu X, Tang J, Qi GJ, Li Z, Jiang YG, Yan S (2018) Image classification with tailored fine-grained dictionaries. IEEE Trans Circuits Syst Video Technol 28(2):454–467. https://doi.org/10.1109/TCSVT.2016.2607345

    Article  Google Scholar 

  43. Mairal J, Ponce J, Sapiro G, Zisserman A, Bach F (2009) Supervised dictionary learning. In: Koller D., Schuurmans D., Bengio Y., Bottou L (eds) Advances in neural information processing systems, vol 21. Curran Associates Inc

  44. Zhou N, Fan J (2014) Jointly learning visually correlated dictionaries for large-scale visual recognition applications. IEEE Trans Pattern Anal Mach Intell 36 (4):715–730. https://doi.org/10.1109/TPAMI.2013.189https://doi.org/10.1109/TPAMI.2013.189

    Article  MathSciNet  Google Scholar 

  45. Zhang Z, Xu Y, Shao L, Yang J (2018) Discriminative block-diagonal representation learning for image recognition. IEEE Transactions on Neural Networks and Learning Systems 29(7):3111–3125. https://doi.org/10.1109/TNNLS.2017.2712801

    Article  MathSciNet  Google Scholar 

  46. Chen Z, Wu XJ, Kittler J (2022) Fisher regularized ε-dragging for image classification. IEEE Transactions on Cognitive and Developmental Systems (Early Access) 1–1. https://doi.org/10.1109/TCDS.2022.3175008

  47. Wang X, Gu Y (2017) Cross-label suppression: a discriminative and fast dictionary learning with group regularization. IEEE Trans Image Process 26(8):3859–3873. https://doi.org/10.1109/TIP.2017.2703101

    Article  MathSciNet  MATH  Google Scholar 

  48. Jiang W, Zhang Z, Qin J, Zhao M, Li F, Yan S (2017) Robust projective dictionary learning by joint label embedding and classification. In: 2017 IEEE International conference on data mining workshops (ICDMW), pp. 510–517. https://doi.org/10.1109/ICDMW.2017.72

  49. Jiang K, Zhao C, Liu Z, Zhu L (2022) Adaptive graph regularized and label embedded dictionary learning for pattern classification. J Electron Imaging 31(3):033028. https://doi.org/10.1117/1.JEI.31.3.033028

    Article  Google Scholar 

  50. Ye J, Zhao Z, Wu M (2007) Discriminative k-means for clustering. In: Proceedings of the 20th international conference on neural information processing systems. NIPS’07, Curran Associates Inc, Red Hook, pp 1649–1656

  51. Li Z, Zhang Z, Qin J, Zhang Z, Shao L (2020) Discriminative fisher embedding dictionary learning algorithm for object recognition. IEEE Transactions on Neural Networks and Learning Systems 31(3):786–800. https://doi.org/10.1109/TNNLS.2019.2910146

    Article  MathSciNet  Google Scholar 

  52. Sadanand S, Corso JJ (2012) Action bank: a high-level representation of activity in video. In: 2012 IEEE Conference on computer vision and pattern recognition, pp. 1234–1241. https://doi.org/10.1109/CVPR.2012.6247806

  53. Ma F, Zhu X, Liu Q, Song C, Jing XY, Ye D (2019) Multi-view coupled dictionary learning for person re-identification. Neurocomputing 348:16–26. https://doi.org/10.1016/j.neucom.2018.07.081, advances in Data Representation and Learning for Pattern Analysis

    Article  Google Scholar 

  54. Wang Q, Guo Y, Wang J, Luo X, Kong X (2018) Multi-view analysis dictionary learning for image classification. IEEE Access 6:20174–20183. https://doi.org/10.1109/ACCESS.2018.2791578

    Article  Google Scholar 

  55. Yu H, Yang Q, Wang G, Xie Y (2022) A novel discriminative dictionary pair learning constrained by ordinal locality for mixed frequency data classification. IEEE Trans Knowl Data Eng 34(10):4572–4585. https://doi.org/10.1109/TKDE.2020.3046114

    Article  Google Scholar 

  56. Tang W, Chouzenoux E, Pesquet JC, Krim H (2022) Deep transform and metric learning network: Wedding deep dictionary learning and neural network. Neurocomputing 509:244–256. https://doi.org/10.1016/j.neucom.2022.08.069

    Article  Google Scholar 

  57. Ding S, Mirza B, Lin Z, Cao J, Lai X, Nguyen TV, Sepulveda J (2018) Kernel based online learning for imbalance multiclass classification. Neurocomputing 277:139–148. https://doi.org/10.1016/j.neucom.2017.02.102https://doi.org/10.1016/j.neucom.2017.02.102, hierarchical Extreme Learning Machines

    Article  Google Scholar 

Download references

Acknowledgements

This work was carried out when the first author was working as the postdoctoral researcher at Xi’an Jiaotong University. This work is partially supported by the Natural Science Basic Research Program of Shaanxi, China (Program No. 2021JM-339).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China

    Kun Jiang, Congyao Zhao, Lei Zhu & Qindong Sun

Authors
  1. Kun Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Congyao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qindong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Jiang.

Ethics declarations

Conflict of Interests

The authors declared that they have no conflicts of interest to this work.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, K., Zhao, C., Zhu, L. et al. Class-oriented and label embedding analysis dictionary learning for pattern classification. Multimed Tools Appl 82, 24919–24942 (2023). https://doi.org/10.1007/s11042-022-14295-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14295-9

Keywords

Search

Navigation