Skip to main content
SpringerLink
Log in

Learning Robust Weighted Group Sparse Graph for Discriminant Visual Analysis

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Recently, sparse representation (SR) based graph has been successfully applied for dimensionality reduction (DR). However, the unsupervised characteristic of SR may cause instable representation results, which is undesired for graph construction. To alleviate the problem, a robust weighted group sparse representation (RWGSR) method is developed by minimizing the combination of l1-norm regularized representation fidelity and the weighted l2,1-norm regularized representation coefficients. RWGSR can find the robust and stable intrinsic intra-class and inter-class adjacent relations of samples. The intra-class and inter-class representations of RWGSR are then utilized to construct corresponding intra-class and inter-class graphs. With the graphs, a novel supervised DR algorithm named robust weighted group sparse graph based embedding (RWGSE) is proposed. Benefitting from RWGSR, RWGSE considers both intra-class and inter-class intrinsic structures of data, and seeks a low-dimensional subspace by simultaneously minimizing the intra-class scatter and maximizing the inter-class scatter. Extensive experiments on public benchmark face and object datasets show the effectiveness of the proposed method.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Zhang L, Zhang D (2016) Visual understanding via multi-feature shared learning with global consistency. IEEE Trans Multimed 18(2):247–259

    Article  Google Scholar 

  2. Sha C, Zhao H (2017) Design and analysis of associative memories based on external inputs of continuous bidirectional associative networks. Neurocomputing 266:433–444

    Article  Google Scholar 

  3. Zhang L, Zhang D (2017) Evolutionary cost-sensitive extreme learning machine. IEEE Trans Neural Netw Learn Syst 28(12):3045–3060

    Article  MathSciNet  Google Scholar 

  4. Donoho DL, et al (2000) High-dimensional data analysis: the curses and blessings of dimensionality. In: AMS math challenges lecture, pp 1–32

  5. Zhang L, Zhang D (2015) Domain adaptation extreme learning machines for drift compensation in E-nose systems. IEEE Trans Instrum Meas 64(7):1790–1801

    Article  Google Scholar 

  6. Wang L, Chen S (2017) Joint representation classification for collective face recognition. Pattern Recognit 63(5):182–192

    Article  Google Scholar 

  7. Zhang Q, Deng K, Chu T (2016) Sparsity induced locality preserving projection approaches for dimensionality reduction. Neurocomputing 200(C):35–46

    Google Scholar 

  8. Kasun LLC, Yang Y, Huang GB et al (2016) Dimension reduction with extreme learning machine. IEEE Trans Image Process 25(8):1–1

    Article  MathSciNet  MATH  Google Scholar 

  9. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2(1):37–52

    Article  Google Scholar 

  10. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  11. Lu GF, Zou J, Wang Y (2016) A new and fast implementation of orthogonal LDA algorithm and its incremental extension. Neural Process Lett 43(3):687–707

    Article  Google Scholar 

  12. Lu GF, Wang Y, Zou J (2016) Graph maximum margin criterion for face recognition. Neural Process Lett 44(2):1–19

    Article  Google Scholar 

  13. Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  14. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  15. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Proceedings of the 14th international conference on neural information processing systems: natural and synthetic, Vancouver, British Columbia, Canada, 03–08 Dec 2001, pp 585–591

  16. He X, Niyogi P (2002) Locality preserving projections. Adv Neural Inf Process Syst 16(1):186–197

    Google Scholar 

  17. He X, Cai D, Yan S, Zhang H-J (2005) Neighborhood preserving embedding. In: The 10th IEEE international conference on computer vision, 2005, vol 2. IEEE, Beijing, pp 1208–1213

  18. Yan S, Xu D, Zhang B, Zhang H-J (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51

    Article  Google Scholar 

  19. Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J Mach Learn Res 8(1):1027–1061

    MATH  Google Scholar 

  20. Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan SC (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98:1031–1044

    Article  Google Scholar 

  21. Cheng B, Yang J, Yan S et al (2010) Learning with l 1-graph for image analysis. IEEE Trans Image Process 19(4):858–866

    Article  MathSciNet  MATH  Google Scholar 

  22. Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recognit 43(1):331–341

    Article  MATH  Google Scholar 

  23. Wei L, Xu F, Wu A (2013) Weighted discriminative sparsity preserving embedding for face recognition. Knowl Based Syst 57(2):136–145

    Google Scholar 

  24. Huang H, Yang M (2015) Dimensionality reduction of hyperspectral images with sparse discriminant Embedding. IEEE Trans Geosci Remote Sens 53(9):5160–5169

    Article  Google Scholar 

  25. Zang F, Zhang JS (2011) Discriminative learning by sparse representation for classification. Neurocomputing 74:2176–2183

    Article  Google Scholar 

  26. Lu GF, Jin Z, Zou J (2012) Face recognition using discriminant sparsity neighborhood preserving Embedding. Knowl Based Syst 31:119–127

    Article  Google Scholar 

  27. Lai J, Jiang X (2016) Classwise sparse and collaborative patch representation for face recognition. IEEE Trans Image Process 25(7):3261–3272

    Article  MathSciNet  MATH  Google Scholar 

  28. Grave É, Obozinski G, Bach F (2011) Trace lasso: a trace norm regularization for correlated designs. In: Proceedings of the 24th international conference on neural information processing systems, Granada, Spain, 12–15 Dec 2011, pp 2187–2195

  29. Wright J, Yang AY, Ganesh A et al (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  30. He W, Zhang H, Zhang L et al (2017) Weighted sparse graph based dimensionality reduction for Hyperspectral images. IEEE Geosci Remote Sens Lett 13(5):686–690

    Article  Google Scholar 

  31. Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781

    Article  Google Scholar 

  32. Guo T, Zhang L, Tan X (2017) Neuron pruning-based discriminative extreme learning machine for pattern classification. Cognit Comput 9(4):581–595

    Article  Google Scholar 

  33. Ly NH, Qian D, Fowler JE (2014) Sparse graph-based discriminant analysis for hyperspectral imagery. IEEE Trans Geosci Remote Sens 52(7):3872–3884

    Article  Google Scholar 

  34. Gao S, Tsang WH, Chia LT (2013) Laplacian sparse coding, hypergraph laplacian sparse coding, and applications. IEEE Trans Pattern Anal Mach Intell 35(1):92–104

    Article  Google Scholar 

  35. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol) 68(1):49–67

    Article  MathSciNet  MATH  Google Scholar 

  36. Yang J, Wright J, Huang T, Ma Y (2010) Image super-resolution via sparse representation of raw patches. IEEE Trans Image Process 19(11):2861–2873

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhang L, Zhang D (2016) Robust visual knowledge transfer via extreme learning machine based domain adaptation. IEEE Trans Image Process 25(10):4959–4973

    Article  MathSciNet  MATH  Google Scholar 

  38. Zhang L, Zuo W, Zhang D (2016) LSDT: latent sparse domain transfer learning for visual adaptation. IEEE Trans Image Process 25(3):1177–1191

    Article  MathSciNet  MATH  Google Scholar 

  39. Xu Y, Fang X, Wu J et al (2015) Discriminative transfer subspace learning via low-rank and sparse representation. IEEE Trans Image Process 25(2):850–863

    Article  MathSciNet  MATH  Google Scholar 

  40. Donoho D (2006) For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun Pure Appl Math 59(6):797–829

    Article  MathSciNet  MATH  Google Scholar 

  41. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58(1):267–288

    MathSciNet  MATH  Google Scholar 

  42. Lin Z, Chen M, Wu L, Ma Y (2009) The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices, UIUC. Technical Report, UILU-ENG-09-2215

  43. Deng W, Yin W, Zhang Y (2011) Group sparse optimization by alternating direction method. Department of Computational and Applied Mathematics, Rice University, Houston, Technical Report, TR11-06

  44. Cai D, He X, Han J, Zhang H-J (2006) Orthogonal laplacianfaces for face recognition. IEEE Trans Image Process 15(11):3608–3614

    Article  Google Scholar 

  45. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  46. Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618

    Article  Google Scholar 

  47. Nene SA, Nayar SK, Murase H (1996) Columbia object image library (COIL-20). Technical Report, CUCS-005-96

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and the editors for their valuable suggestions. This work was supported by the National Natural Science Foundation of China (Nos. 61571069, 61771079), Chongqing University Postgraduates’ Innovation Project (No. CYB15030), and in part by the Fundamental Research Funds for the Central Universities (Nos. 106112017CDJQJ168817, 106112017CDJQJ168819).

Author information

Authors and Affiliations

  1. College of Communication Engineering, Chongqing University, Chongqing, 400044, China

    Tan Guo, Xiaoheng Tan, Lei Zhang, Qin Liu, Lu Deng & Chaochen Xie

Authors
  1. Tan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoheng Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chaochen Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tan Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, T., Tan, X., Zhang, L. et al. Learning Robust Weighted Group Sparse Graph for Discriminant Visual Analysis. Neural Process Lett 49, 203–226 (2019). https://doi.org/10.1007/s11063-018-9809-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9809-5

Keywords

Search

Navigation